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1 Introduction

2 mathematical model of a pneumatic system, 3 development of a cascade control system on a pneumatic actuator system, 4 stability analyses, 5 results and performance analyses, 6 conclusions, modeling and cascade control of a pneumatic positioning system.

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Mandali, A., and Dong, L. (March 18, 2022). "Modeling and Cascade Control of a Pneumatic Positioning System." ASME. J. Dyn. Sys., Meas., Control . June 2022; 144(6): 061004. https://doi.org/10.1115/1.4053966

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A robust cascade control system based on active disturbance rejection controller (ADRC) is originally developed for accurate position control of a pneumatic servo system, which is usually characterized by nonlinearity, uncertainty, and disturbance. The proposed control system consists of inner and outer control loops. Particularly, a linear ADRC (LADRC) is utilized to adjust valve position for a linear spool valve dynamics in the inner loop. A nonlinear ADRC (NADRC) is designed to control the position of a nonlinear pneumatic actuator subsystem in the outer loop. LADRC and NADRC contain linear and nonlinear extended state observers (ESOs), respectively. The ESO can estimate both unknown nonlinear dynamics and external disturbances and thus make the ADRC robust against system uncertainties and disturbances. Simulation results successfully demonstrate the effectiveness and robustness of the proposed cascade control system. The stabilities of the inner and outer control loops were proved separately using a Lyapunov approach.

Pneumatic actuators utilize compressed air to generate linear or rotary motion. They mainly consist of a piston, a cylinder, and valves. A pneumatic positioning (or servo) system is an integration of pneumatic actuator, a servo controller, air pressure sensors, and position sensors. Pneumatic servo systems have been widely used in various industrial applications such as drilling, pressing, robotics [ 1 , 2 ], rehabilitation devices [ 3 ], air brakes, pneumatic muscle actuators [ 4 ], and so on. They provide a compact and cost-effective solution to industry for point-to-point actions. In addition, pneumatic actuators are low-maintenance, reliable, and environmentally friendly. They have longer durability when compared to hydraulic and electric actuators. Despite these advantages, pneumatic actuators exhibit highly nonlinear characteristics due to the compressibility of air, the presence of friction, and nonlinear pressure dynamics [ 5 ]. “These nonlinearities make an accurate position control of a pneumatic actuator difficult to achieve [ 6 ].” In addition, a sudden loss of air pressure caused by manufacturing defect in cylinder and valves introduces external disturbances to the pneumatic system [ 7 ]. Mechanical wear of cylinder, piston, and other parts result in parameter variations (or system uncertainty). Both external disturbances and system uncertainties pose further challenges to control system design. In the past decades, many advanced control techniques have been developed to improve the accuracy of a pneumatic positioning system. These control techniques include but are not limited to neural network-based nonlinear proportional-integral-derivative (PID) control [ 8 ], sliding mode control [ 7 , 9 – 11 ], adaptive control [ 12 , 13 ], backstepping control [ 14 ], linear active disturbance rejection control (LADRC) [ 7 , 15 , 16 ], and nonlinear ADRC (NADRC) [ 17 , 18 ]. Besides single-loop control structure [ 7 – 18 ], cascade control structure is presented in Refs. [ 19 ] and [ 20 ], where inner loop is pressure control and outer loop is piston position control. Specifically, classic proportional-derivative (PD) controllers, nonlinear PID controllers, and sliding mode controls are utilized in Refs. [ 19 ] and [ 20 ] to control the air pressure and piston position separately. Although most of the reported studies [ 6 , 8 – 15 , 17 – 19 ] deal with some nonlinearities in pneumatic actuators, they disregard system uncertainties. In addition, the spool dynamic model is either neglected [ 7 , 12 , 16 , 17 ] or partially considered in the controller designs [ 10 ]. Particularly, in Refs. [ 10 ] and [ 20 ], a linear relationship is assumed between input valve voltage and output orifice area of proportional valves. However, in practice, this relationship is nonlinear due to the dead zone in valves.

As indicated in Refs. [ 21 – 23 ], a crucial element of a servo pneumatic system is proportional valve. Unlike on/off valves [ 11 ], which have two positions, proportional valves can be shifted to multiple positions for routing gas between different combinations of inlet and outlet ports. They can regulate air delivery with great precision to ensure that the required position be achieved. In this paper, a proportional spool valve is utilized. Varying the spool position can adjust the air flow in two chambers of a pneumatic cylinder. A complete nonlinear model of the spool valve is derived. A control system is developed based on the complete model of spool valve as well as piston–load dynamic model and nonlinear pressure model. Here, a novel cascade control structure is created that is different from Refs. [ 19 ] and [ 20 ]. In the inner loop, a LADRC is designed to control the linear spool dynamics. In the outer loop, a NADRC is developed to cope with the nonlinearities present in the valve and pressure dynamics. The key component of ADRC is extended state observer (ESO). LADRC contains a linear ESO (LESO). NADRC has a nonlinear ESO (NESO). NESO can approximate both unknown nonlinear dynamics and external disturbance and thus make the NADRC robust against system uncertainties and disturbances. Similarly, a LESO can improve the robustness of LADRC. Both LADRC and NADRC have been individually applied to power systems [ 24 ], micro-electromechanical systems [ 25 , 26 ], and mechanical motion systems [ 7 , 15 – 18 ]. However, the combination of LADRC and NADRC has not been applied to a pneumatic positioning system. In this paper, we originally employ LADRC to adjust the spool position, and NADRC to accurately control the piston position. In addition, the convergences of estimation error for the ESOs and tracking error for the ADRCs are successfully proved based on the nonlinear pneumatic actuator model.

The rest of this paper is organized as follows. The pneumatic system is modeled in Sec. 2 . A cascade control structure with both LADRC and NADRC is developed in Sec. 3 . The stability of the proposed control system is presented in Sec. 4 . Simulation results are shown in Sec. 5 . In Sec. 6 , concluding remarks are made, and future research is suggested.

The basic schematic model of a pneumatic system is shown in Fig. 1 . It consists of a pneumatic actuator (double acting pneumatic cylinder), load, proportional spool valve, and position and pressure sensors. The pneumatic actuator contains a piston, which divides the cylinder into two chambers: chamber 1 and chamber 2. A piston rod is attached to the center of the piston toward chamber 2. It is connected to an external load. The 5/3 proportional spool valve has five ports which can be connected in three positions. In the midposition (position 2), all the ports of the valve are blocked. At position 1, the supply pressure is connected to chamber 1, and the exhaust pressure is connected to chamber 2 and vice versa for position 3. Two ports of the valve are connected to the two chambers of the cylinder through connecting tubes. The other three ports of the valve are connected to two exhaust pipes and a compressed air source, respectively. The pressure of air flow through this exhaust pipe is denoted as P a . The supply air pressure is represented by P s . The valve changes its position based on the input voltage supplied to it. The pressure difference between two chambers will result in the movement of the piston-rod and load assembly. The following assumptions are made while deriving the mathematical model of the pneumatic system:

Schematic model of a pneumatic servo system

Schematic model of a pneumatic servo system

The air in cylinder is assumed to be an ideal gas.

The effects of connecting tubes on the dynamic behavior of the system are neglected under the assumption that the air supply is very close to the valve and cylinder [ 23 ].

The temperature and pressure inside each chamber are homogeneous.

The air flow is one-dimensional.

Kinetic and potential energy terms are negligible.

2.1 Piston-Rod and Load Dynamics.

The piston-rod and load assembly are shown in Fig. 2(a) . The free-body diagram of the assembly is shown in Fig. 2(b) , where the force acting on the piston in each chamber is represented as the product of the pressure ( P ) in that chamber and the cross section area ( A ) of the chamber. In Fig. 2 , M p is the mass of the piston and piston-rod assembly, M L is the mass of the external load, β is the viscous friction coefficient, X is the piston displacement, L is the length of the cylinder, F L is the external force acting on the load, F f is the Coulomb friction force, P 1 and P 2 are the absolute pressures in chambers 1 and 2, A 1 and A 2 are the effective piston areas, P a is the absolute ambient pressure, A r is the cross section area of rod, and V 1 and V 2 are the volumes of chambers 1 and 2, respectively.

Piston-rod and load: (a) physical assembly and (b) free-body diagram of the physical assembly

Piston-rod and load: ( a ) physical assembly and ( b ) free-body diagram of the physical assembly

In Eq. (1) , P a A r is the force acting on the rod due to the atmospheric pressure. Equation (1) represents the mathematical model of piston–load dynamics.

2.2 Pressure Dynamics in the Pneumatic Actuator.

where i  = 1, 2. Equations (10) and (11) represent the nonlinear pressure dynamic model.

2.3 Valve Dynamics.

A proportional valve plays a crucial role in commanding the pneumatic actuator. The valve dynamics depends on the mass flow rate through orifices and spool dynamics.

where n h is the number of active holes for air path in the sleeve, R h is the radius of hole, 2 P w is the spool width, and X s is the spool position. Equations (12) – (16) represent the nonlinear model of mass flow rate.

Spool valve: (a) physical assembly at neutral position and (b) equivalent representation of the physical assembly

Spool valve: ( a ) physical assembly at neutral position and ( b ) equivalent representation of the physical assembly

The equivalent electric circuit of the voice coil motor is shown in Fig. 4 .

Equivalent circuit of the servo motor [31]

Equivalent circuit of the servo motor [ 31 ]

Equation (23) represents a linear spool dynamic model.

A cascade controller is developed for the pneumatic positioning system where the pneumatic actuator is strongly coupled with spool valve. This cascade controller consists of inner and outer control loops as shown in Fig. 5 , in which the control signal of the outer loop is acting as the reference signal ( X sref ) to inner loop. The inner loop consists of a LADRC and a linear spool dynamic, which is given by Eq. (23) with voltage v as the control input and spool position X s as output. The LADRC is designed to control the spool position to its desired position X sref . The output of the inner loop ( X s ) determines the variable orifice areas of the valve which in turn defines the mass flow rate through each chamber. Depending on the mass flow rate, a pressure difference is built up in the actuator resulting in the displacement ( X ) of piston rod. The outer loop contains piston–load dynamics (given by Eq. (1) ), nonlinear pressure dynamics (given by Eqs. (10) and (11) ), and nonlinear mass flow rate through the orifice areas of the valve (given by Eqs. (12) – (16) ). Due to the presence of the nonlinearities in the outer loop, a NADRC is designed to drive the displacement of piston rod ( X ) to its reference level ( X ref ). The control goal of NADRC is to determine the reference spool position in order to reduce the tracking error in piston position control.

Cascade controller schematics for a pneumatic system

Cascade controller schematics for a pneumatic system

3.1 Inner Control Loop Design.

where the observer gain vector is L ̂  = [ L 1 , L 2 , L 3 ] T  = [3 ω o , 3 ω o 2 , ω o 3 ] T , and ω o is the observer bandwidth. By adjusting the observer bandwidth, the estimated state ζ 1 will converge to the spool position X s . The observer gain vector is chosen in such a way that the characteristic equation of the LESO will be ( s  +  ω o ) 3 , where ω o is positive.

where K  = [ k 1 , k 2 , 1] = [ ω c 2 , 2 ω c , 1 ] is the controller gain vector with ω c as the positive controller bandwidth. The controller gains are chosen in such a way that the characteristic equation of the feedback controller is ( s  +  ω c ) 2 .

3.2 Outer Control Loop Design.

The nonlinear gain g ( X ) is bounded as the mass flow rate and orifice area are bounded. The term g ( X ) can be estimated by b ̂ 2   [ 7 ].

where k ̂ 1 , k ̂ 2 ,   and k ̂ 3 are the controller gains. Controller gain vector is K ̂ = [ k ̂ 1 , k ̂ 2 , k ̂ 3 , 1 ] = [ Ω c 3 , 3 Ω c 2 , 3 Ω c , 1 ] with a positive tuning parameter Ω c as the controller bandwidth.

4.1 Convergence of Nonlinear Extended State Observer.

From Eq. (59) , the derivative of the Lyapunov function is negative definite when ‖ e ‖ > 8 Q ¯ ϵ λ max ( N ) ⁠ . In addition, the terms e , e 1 , e 2 , e 3 , e 4 , and φ ( e 1 ) are bounded. So, V ¯ ˙ 1   is uniformly continuous, and V ¯ ¨ 1   is also bounded. Invoking Barbalat's lemma [ 35 ], the error vector e  = [ e 1 , e 2 , e 3 , e 4 ] T will converge to zero as time goes to infinity and ‖ e ‖ > 8 Q ¯ ϵ λ max ( N ) ⁠ . This proves the effectiveness of the NESO.

4.2 Stability of the Closed-Loop System With Nonlinear Active Disturbance Rejection Control.

The derivative of Lyapunov function V ̂ 2 ( η ) is negative definite as ‖ η ‖ > 16 ( L ¯ + 1 ) Q ¯ ϵ λ max ( N ) λ max ( P t ) ⁠ . In addition, the terms η , η 1 , η 2 , η 3 , e , e 1 , e 2 , e 3 , and e 4 are bounded. So, V ¯ ˙ 2   is uniformly continuous, and V ¯ ¨ 2   is bounded. Invoking Barbalat's lemma [ 35 ], the tracking error vector η  = [ η 1 , η 2 , η 3 ] T will converge to zero as time goes to infinity and ‖ η ‖ > 16 ( L ¯ + 1 ) Q ¯ ϵ λ max ( N ) λ max ( P t ) ⁠ . This proves the validity of NADRC.

In this section, we utilize matlab / simulink software to simulate the pneumatic positioning system in Fig. 5 based on the mathematical model derived in Sec. 2 and the cascade controller designed in Sec. 3 . Our control objective is to drive the piston position to its desired position by controlling the spool position. The system parameters are listed in Table 1 , and their values are based on a real pneumatic system [ 7 , 31 ].

Parameter values [ 7 , 31 ]

A multistep reference signal is provided to the piston–load subsystem. The displacement response and the reference signal are shown in Fig. 6 , where the solid line represents the reference trajectory and dotted line represents the actual piston position output X . From Fig. 6 , we can see that the pneumatic system controlled by the cascade controller tracks the reference signal accurately.

Piston position

Piston position

The performance of the outer loop piston position is indirectly controlled by the inner loop spool position. The inner loop spool position and its reference signal are shown in Fig. 7 , where the solid line represents the reference spool position ( X sref ) and the dotted line represents the actual spool position ( X s ). The spool position is limited by ±1.1 mm in reality [ 31 ]. So, the reference signal has an amplitude of 1.1 mm. From Fig. 7(a) , we can see that as the piston position changes from 0.09 m to 0.01 m at t  = 2 s, the spool position is negative until the piston position reaches its desired position. This negative spool position indicates that the supply gas is connected to chamber 2 and chamber 1 is connected to exhaust, resulting in the retraction of the piston. Although a time delay of 0.9 ms is observed between reference and actual spool positions in Fig. 7(b) , it does not affect the tracking performance of piston position.

(a) Spool position and (b) zoomed-in view of spool position

( a ) Spool position and ( b ) zoomed-in view of spool position

Due to the change in the mass flow rate in each chamber, the chamber pressures vary accordingly. The chamber pressures P 1 and P 2 are shown in Fig. 8 , where the solid line represents the pressure in chamber 1 ( P 1 ) and the dotted line represents the pressure in chamber 2 ( P 2 ). Due to the pressure difference in these chambers, the total force exerted on the piston will cause the movement of the piston. When this total force is greater than zero, piston extends. When it is less than zero, the piston retracts. As the total force is zero, the piston stays at its current position.

Chamber pressures

Chamber pressures

Figures 6 – 8 demonstrate that the proposed cascade controllers can successfully drive the piston to its desired position by controlling the spool position.

The robustness of the cascade controller is tested with external disturbance. One of the most common external disturbances is caused by air leak of the cylinder. The leak could be from a loose connection, a hole in the tubing, or wear to a seal on the pneumatic actuator. This sudden air leak is represented by a constant step disturbance which is added to chamber 2 pressure model of the actuator at t  = 2 s. The response of the pneumatic system in the presence of external disturbance is illustrated in Fig. 9 . As shown in Fig. 9(b) , when an external disturbance occurs, there is an overshoot percentage of 0.05% in piston position, and it takes about 0.1 s for the system to return to its desired position.

Response with external disturbance: (a) piston position and (b) zoomed-in view of piston position after disturbance is added

Response with external disturbance: ( a ) piston position and ( b ) zoomed-in view of piston position after disturbance is added

The chamber pressures P 1 and P 2 in the presence of external disturbance are shown in Fig. 10 , where a slight drop in the chamber pressures at t  = 2 s is notable due to air leak. Figures 9 and 10 demonstrate that the proposed control system is robust against external disturbances.

Chamber pressures in the presence of external disturbance

Chamber pressures in the presence of external disturbance

The net force acting on the cylinder when subjected to external disturbance is shown in Fig. 11 . From this figure, we can see that there is a spike at t  = 2 s, when the disturbance is added to the system (i.e., pressure leak occurs). This spike represents a contrast force acting on the cylinder rod against the disturbance.

Net force acting on the cylinder in the presence of external disturbance

Net force acting on the cylinder in the presence of external disturbance

To test the robustness of the proposed controller against system uncertainties, we varied the parameter values of M L , A r , A 1 , A 2 , and M s simultaneously from −40% to +60% of their nominal values. Parameter variations represent mechanical wear of the cylinder, piston rod, and other parts in real-world situation. The piston position of the pneumatic system when the parameters are varied is shown in Fig. 12 . From Fig. 12 , the proposed controller is robust against parameter variations

Pneumatic system's response with parameter variations

Pneumatic system's response with parameter variations

In this paper, a cascade control system that integrates LADRC with NADRC is originally developed on a highly nonlinear pneumatic actuator system that is subject to parameter variations and external disturbances. Our goal is to achieve accurate position control for this nonlinear pneumatic actuator despite the presences of disturbances and system uncertainties. To reach the control goal, a NADRC is developed to deal with nonlinearity in the outer loop, and a LADRC is designed to adjust the displacement of a proportional valve in the inner loop. As a result, the valve commands the actuator to reach a desired position. Both LADRC and NADRC are robust against internal and external perturbations because LESO and NESO can estimate the perturbations in real-time and send the estimated signal to controllers. Simulation results verify the effectiveness and robustness of the proposed control system. Furthermore, the stabilities of inner and outer control loops are proved, respectively, using Lyapunov approach. In the future, we plan to perform robustness test of the proposed control system on a real pneumatic actuator. We also plan to further tune the controllers in hardware experiments to improve servo response.

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  • Original Article
  • Open access
  • Published: 09 April 2020

Analysis of Power Matching on Energy Savings of a Pneumatic Rotary Actuator Servo-Control System

  • Yeming Zhang   ORCID: orcid.org/0000-0001-8649-044X 1 ,
  • Hongwei Yue 1 ,
  • Ke Li 2 &
  • Maolin Cai 3  

Chinese Journal of Mechanical Engineering volume  33 , Article number:  30 ( 2020 ) Cite this article

1519 Accesses

3 Citations

Metrics details

When saving energy in a pneumatic system, the problem of energy losses is usually solved by reducing the air supply pressure. The power-matching method is applied to optimize the air-supply pressure of the pneumatic system, and the energy-saving effect is verified by experiments. First, the experimental platform of a pneumatic rotary actuator servo-control system is built, and the mechanism of the valve-controlled cylinder system is analyzed. Then, the output power characteristics and load characteristics of the system are derived, and their characteristic curves are drawn. The employed air compressor is considered as a constant-pressure source of a quantitative pump, and the power characteristic of the system is matched. The power source characteristic curve should envelope the output characteristic curve and load characteristic curve. The minimum gas supply pressure obtained by power matching represents the optimal gas supply pressure. The comparative experiments under two different gas supply pressure conditions show that the system under the optimal gas supply pressure can greatly reduce energy losses.

1 Introduction

The problem of energy shortages has become increasingly significant with the rapid development of society. In addition to discovering new energy sources, energy conservation is the most effective and important measure to fundamentally solve the energy problem [ 1 ]. Energy saving has increasingly become a hot topic of concern. Energy has always been a constraint to economic development, which makes energy-saving research more urgent and practical [ 2 ]. Currently, pneumatic technology is widely used in various fields of industry, and has become an important technical means of transmission and control [ 3 , 4 ]. The use of existing technology to improve the energy utilization rate of energy-consuming equipment is an important energy-saving method [ 5 ].

However, the energy efficiency of pneumatic technology is relatively low [ 6 ]. Therefore, improving the efficiency of energy utilization and reducing the energy loss of pneumatic systems have become the concern of scholars all over the world [ 7 , 8 ].

Pneumatic systems have three aspects of energy wastage [ 9 , 10 ]: (1) gas and power losses during compressor gas production, (2) pressure loss in the gas supply pipeline, and (3) gas leakage from the gas equipment [ 11 ]. Accordingly, many methods are available to solve these problems. For the pressure loss in the air source, the timing of opening and closing of multiple air compressors can be optimized, and the gas production process of the air compressors can also be optimized, such as making full use of the expansion of compressed air to reduce unnecessary power consumption [ 12 ]. In order to reduce pressure loss in the pipeline, the method of reducing the pressure in the gas supply pipeline can be adopted [ 13 ]. When necessary, a supercharger can be added in front of the terminal equipment. For gas leakage from the gas equipment, optimizing the component structure is usually implemented to solve this problem. The pneumatic servo-control system precisely controls the angle of rotation; however, energy loss still occurs in the system. For this system, reducing the gas supply pressure is the most effective way of reducing the energy loss. Determining the critical pressure and reducing the gas supply pressure as much as possible while ensuring normal operation of the system are the key. The power-matching method can solve the optimization problem of the gas supply pressure based on the power required by the system [ 14 ]. In flow compensation, different compensation controllers can also be designed to match the flow and the system to realize the purpose of energy savings [ 15 , 16 ]. Problems arise with regard to the high energy consumption and poor controllability of the rotary system of a hydraulic excavator due to throttle loss and overflow loss in the control valve during frequent acceleration and deceleration with large inertia. Therefore, Huang et al. [ 17 ] proposed the flow matching of a pump valve joint control and an independent measurement method of the hydraulic excavator rotary system to improve the energy efficiency of the system and reduce throttle loss. Xu et al. [ 18 ] designed a dynamic bypass pressure compensation circuit of a load sensing system, which solved the problems of pressure shock and energy loss caused by excessive flow and improved the efficiency and controllability of the system. Kan et al. [ 19 ] analyzed the basic characteristics of a hydraulic transmission system for wheel loaders using numerical calculation and adopted the optimal design method of a power-matching system. This improved the efficient working area of the system and average efficiency in the transportation process, and reduced the average working fuel consumption rate. Yang et al. designed an electro-hydraulic flow-matching controller with shunt ability to improve the dynamic characteristics and energy-saving effect and improve the stability of the system [ 20 ]. Guo et al. [ 21 ] used genetic algorithm to optimize the parameters of an asynchronous motor to achieve energy savings and consumption reduction, which proved the effectiveness and practicability of the power matching method of an electric pump system. Wang et al. [ 22 ] matched an engine and a generator to achieve efficiency optimization and obtained a common high efficiency area. They proposed a partial power tracking control strategy. Lai et al. [ 23 ] proposed a parameter matching method for an accumulator in a parallel hydraulic hybrid excavator and optimized the parameter matching process of the main components such as the engine, accumulator, and hydraulic secondary regulatory pump using genetic algorithm to reduce the installed power. Yan et al. [ 24 ] focused on the problem in which the flow of a constant displacement pump could not match with the changing load, resulting in energy loss. They proposed an electro-hydraulic flow-matching steering control method, which used a servo motor to drive a constant displacement pump independently to reduce the energy consumption of the system. At present, many studies on energy savings are conducted using the power matching method in the hydraulic system, but only few focus on the pneumatic system [ 25 ].

In the present study, a method of reducing the gas supply pressure is implemented to reduce energy loss of a pneumatic rotary actuator servo-control system. The output and load characteristic curves of the system are derived, and the power source characteristic curve is matched to determine the optimal gas supply pressure. Finally, the experiment verifies the energy-saving effect under this gas supply pressure.

Through theoretical analysis and experimental verification of the application platform of the pneumatic rotary actuator, a method of function matching and energy optimization method for the pneumatic rotary actuator under normal working conditions is proposed for the first time.

2 Experimental Platform

Figure  1 shows the schematic diagram of the pneumatic rotary actuator servo-control system.

figure 1

Schematic diagram of the pneumatic rotary actuator servo-control system

As a gas source, the air compressor provides power to the system. The air filter, air regulator, and air lubricator are used to filter and clean the gas. When the driving voltage signal of the proportional directional control valve is given, the proportional valve controls the flow and direction of the gas, and then controls the rotary motion of the pneumatic rotary actuator. The rotary encoder measures the angular displacement and transmits the TTL (Transistor–Transistor Logic) level signals to the data acquisition card. The data acquisition card is installed in the industrial personal computer which calls the program of the upper computer, samples the encoder signal, and outputs a 0–10 V voltage signal through the controller calculation. The driving voltage signal output by the controller further regulates the flow and direction of the proportional directional control valve to reduce the angle error. After continuous iteration, the angle error of the system decreases and tends to stabilize.

Figure  2 shows the experimental platform of the pneumatic rotary actuator servo-control system. The round steel passes through the pneumatic rotary actuator and is connected to the rotary encoder through the coupling. The pneumatic rotary actuator is horizontally installed.

figure 2

Experimental diagram of the pneumatic rotary servo-control system

By selecting the MPYE-M5-010-B model proportional valve with a smaller range, we can more easily ensure the control accuracy of the system. The SMC MSQA30A pneumatic rotary actuator is adopted. The actuator has a high-precision ball bearing and belongs to a high-precision actuator type. The rotating platform of the actuator contains many symmetrical threaded holes for easy introduction of loads. A high-precision rotary encoder is used, and the 20000P/R resolution corresponds to an accuracy of 1.8 × 10 −2 °, which satisfies the high-precision measurement for the rotation angle. In addition, the air compressor and the filter, regulator, and lubricator (F. R. L.) units satisfy the gas supply pressure of at least 0.8 MPa. The digital I/O port and analog output port of the data-acquisition card must meet the experimental requirements, and the higher digit counter in the data-acquisition card improves the system response speed. The models and parameters of the components are listed in Table  1 .

In some experimental tests, measuring the flow rate, pressure, and temperature of the gas is necessary, which can be performed using a flow sensor, a pressure transmitter, and a temperature transmitter (thermocouple), respectively. The flow rate in the inlet and outlet is measured using a flow sensor in the FESTO SFAB series with a range of 2–200 L/min, and the flow rate of the leak port is measured using a flow sensor with a range of 0.1–5 L/min in the SFAH series. The MIK-P300 pressure transmitter has high accuracy and fast response and can accurately measure the pressure changes. A thermocouple is used as a temperature transmitter to measure the gas temperature. To prevent signal interference, a temperature isolator is added to the circuit for the temperature signal transmission. The models and parameters of the test components are listed in Table  2 . The circuit connection of the experimental platform is shown in Figure  3 .

figure 3

Circuit connection of the experimental platform

The schematic diagram of the valve-controlled cylinder system is constructed according to the experimental platform, as shown in Figure  4 . The system consists of Chamber a and Chamber b . The dashed lines represent the boundaries of the chambers. Figure  4 shows the gas-flow mechanism when the spool moves to the right, and \(\dot{m}_{\text{a}}\) , \(\dot{m}_{\text{b}}\) represent the mass flow rates of Chamber a and Chamber b , respectively. \(p_{\text{a}}\) , \(p_{\text{b}}\) and \(T_{\text{a}}\) , \(T_{\text{b}}\) represent the corresponding pressure and temperature of Chamber a and Chamber b , respectively. \(p_{\text{s}}\) is the gas supply pressure, \(p_{\text{e}}\) is the atmospheric pressure, and \(\theta\) is the rotation angle of the pneumatic rotary actuator.

figure 4

Schematic diagram of the valve-controlled cylinder system

3 Power Characteristic Matching

3.1 output characteristics of the valve-controlled cylinder.

The output characteristic of the valve-controlled cylinder system refers to the relationship between the total load moment and angular velocity when the power source is known. The output characteristic can be obtained by the following method.

When supply pressure \(p_{\text{s}}\) is relatively low, i.e., when \(0.1013\;{\text{MPa}} \le p_{\text{s}} \le 0.4824\;{\text{MPa}}\) , the condition is satisfied, i.e., \({{p_{a} } \mathord{\left/ {\vphantom {{p_{a} } {p_{s} }}} \right. \kern-0pt} {p_{s} }} > b = 0.21\) , where \(b\) denotes the critical pressure ratio. The gas flow in the proportional–directional control valve is a subsonic flow. Here, the mass flow equation through the proportional valve is [ 26 ]

where \(S_{\text{e}}\) is the effective area of the proportional valve orifice, \(R\) is the gas constant, \(T_{\text{s}}\) is the gas supply temperature, and \(\kappa\) is the isentropic index.

When the opening of the proportional–directional control valve is maximum, the mass flow rates of the two chambers are maximum, which can be expressed as

where \(C\) is the flow coefficient and \(r\) is the radius of the orifice.

Under adiabatic condition, \({{p_{\text{a}} } \mathord{\left/ {\vphantom {{p_{\text{a}} } \rho }} \right. \kern-0pt} \rho }_{\text{a}}^{\kappa } = {{p_{\text{s}} } \mathord{\left/ {\vphantom {{p_{\text{s}} } {\rho_{\text{s}}^{\kappa } }}} \right. \kern-0pt} {\rho_{\text{s}}^{\kappa } }}\) and \({{p_{\text{b}} } \mathord{\left/ {\vphantom {{p_{\text{b}} } \rho }} \right. \kern-0pt} \rho }_{\text{b}}^{\kappa } = {{p_{\text{c}} } \mathord{\left/ {\vphantom {{p_{\text{c}} } {\rho_{\text{c}}^{\kappa } }}} \right. \kern-0pt} {\rho_{\text{c}}^{\kappa } }}\) , where \(\rho_{\text{a}}\) , \(\rho_{\text{b}}\) , \(\rho_{\text{s}}\) , and \(\rho_{\text{e}}\) represent the gas density in Chamber a , gas density in Chamber b , gas supply density, and atmospheric density respectively. For the pneumatic rotary actuator, these can be obtained from the mass flow-rate formulas:

where \(A\) is the effective area of a single piston, \(d_{\text{f}}\) is the pitch diameter of the gear, and \(\dot{\theta }\) is the angular velocity of the pneumatic rotary actuator.

The dynamic equation of the pneumatic rotary actuator can be expressed as follows:

where \(f\) is the total load moment.

Combining Eqs. ( 3 )–( 6 ) yields \(p_{\text{a}}\) and \(p_{\text{b}}\) . Substituting the expressions of \(p_{\text{a}}\) and \(p_{\text{b}}\) into Eq. ( 7 ) yields

Eq. ( 8 ) is the expression of the output characteristic curve of the valve-controlled cylinder. The known parameters in the equation are shown in Table  3 .

To extend and improve the influence of the output characteristics of the system, the influence law of the fixed parameters is also theoretically analyzed. Figure  5 shows the output characteristic curves. The following characteristics can be found in plane \(\dot{\theta }{ - }f\) :

figure 5

Output characteristic curve of the valve-controlled cylinder: ( a ) Output characteristics of the pressure variation, ( b ) Output characteristics of the change in the valve port area, ( c ) Output characteristics of the variation in the effective piston area

Figure  5 (a) shows that when pressure \(p_{\text{s}}\) increases from 0.3 MPa to 0.4 MPa, the curve is a parabola and \(p_{\text{s}}\) is a variable parameter. Increasing \(p_{\text{s}}\) makes the whole parabola move to the right while the shape does not change.

Figure  5 (b) shows that when the maximum opening area of the valve increases from \(\uppi r^{2}\) to \(2\uppi r^{2}\) , the whole parabola becomes wider but the vertices remain the same.

Figure  5 (c) shows that the increase in effective working area \(A\) of the piston makes the top of the parabola move to the right and the parabola simultaneously becomes narrower.

We can see from Eq. ( 8 ) that when \(\dot{\theta }\)  = 0, the maximum total load moment can be expressed as

When \(f\)  = 0, the maximum angular velocity is

3.2 Load Characteristic

The load characteristic refers to the relationship between the moment required for the load to move and the position, velocity, and acceleration of the load itself [ 27 ]. The load characteristic can be expressed by the angular velocity–moment curve.

The load characteristic is related to the form of load movement. When the load sinusoidally moves, the motion of the load is expressed as

where \(\theta_{\text{m}}\) is the maximum angular value of the load motion and \(\omega\) is the sinusoidal motion frequency of the load.

The angular velocity and acceleration of the load are

The total load moment of the pneumatic rotary actuator is

where \(m_{\text{p}}\) is the mass of a single piston and \(J\) is the moment of inertia of the pneumatic rotary actuator. \(F_{\text{f}}\) is the friction force and can be represented by the Stribeck friction model.

where \(F_{\text{s}}\) is the maximum static friction, \(F_{\text{c}}\) is the Coulomb friction, \(\dot{\theta }_{\text{s}}\) is the critical Stribeck velocity, and \(\sigma\) is the viscous friction coefficient.

Combining Eqs. ( 12 )–( 14 ) yields

The known parameters in Eq. ( 16 ) are listed in Table  4 .

The load characteristic curve can be obtained from Eq. ( 16 ) when \(\theta_{\text{m}}\)  = 180° and \(\omega\)  = 10 rad/s, as shown in Figure  6 .

figure 6

Load characteristic curve

3.3 Power Source Characteristics and Matching

The power source characteristic refers to the characteristic of the flow and pressure provided by the power source, which can be expressed by the flow–pressure curve. The air compressor used in this work can be approximately regarded as a constant-pressure source for a quantitative pump. Therefore, the power source characteristic curve is shown in Figure  7 , where \(\dot{m}_{\text{s}}\) is the gas supply mass flow, \(p_{\text{s}}\) is the gas supply pressure, \(\dot{m}_{\text{L}}\) is the driving mass flow, and \(p_{\text{L}}\) is the driving pressure.

figure 7

Power source characteristic curve

The output and power source characteristics of the valve-controlled cylinder should envelope the load characteristic curve. To minimize unnecessary energy consumption, the output characteristic curve should be tangent to the load characteristic curve, and the power source characteristic curve should be tangent to the output characteristic curve in the f -axis direction and the load characteristic curve in the \(\dot{\theta }\) -axis direction, as shown in Figure  8 .

figure 8

Power source characteristic matching

In this manner, the maximum total load moment is obtained, i.e., \(f_{\text{max} }\)  = 0.96 N·m. The optimum gas supply pressure can be obtained from Eq. ( 9 ), i.e., \(p_{\text{s}} { = }{{f_{\text{max} } } \mathord{\left/ {\vphantom {{f_{\text{max} } } {d_{\text{f}} A}}} \right. \kern-0pt} {d_{\text{f}} A}} + p_{\text{e}} { = 0} . 3 3 6 7\;{\text{MPa}}\) .

4 Experimental Verification of the Energy Savings

To verify the calculation results presented in the previous section, low-speed uniform-motion experiments of the pneumatic rotary actuator were carried out using 0.6 and 0.3367 MPa supply pressure. The total energy and effective energy consumed by the valve-controlled cylinder system were measured and calculated. In the experiment, the input-angle signal was set as the slope signal, and Chamber a was used as the intake chamber. The motion curve of the uniform-velocity period was considered, and the angular strokes in the two experiments were the same. Two flow sensors were used to measure the volume flow of the gas supply pipeline and the Chamber a port. Temperature sensors were used to measure the gas temperature of the gas supply pipeline and Chamber a .

Figures  9 and 10 show the system response curves at gas supply pressure values of 0.6 and 0.3367 MPa, respectively, including the angle curve, gas supply flow curve, gas supply temperature curve, pressure curve of Chamber a , volume-flow curve of Chamber a , and temperature curve of Chamber a . Figures  9 (f) and 10 (f) show that the temperature in Chamber a changed with the change in the velocity, which first increased, then decreased, and then entered a stable stage.

figure 9

System-response curve at gas supply pressure of 0.6 MPa: ( a ) Angle curve, ( b ) Gas supply flow, ( c ) Gas supply temperature, ( d ) Pressure curve of Chamber a , ( e ) Volume-flow curve of Chamber a , ( f ) Temperature curve of Chamber a

figure 10

System response curve at gas supply pressure of 0.3367 MPa: ( a ) Angle curve, ( b ) Gas supply flow, ( c ) Gas supply temperature, ( d ) Pressure curve of Chamber a , ( e ) Volume-flow curve of Chamber a , ( f ) Temperature curve of Chamber a

The total power consumed by the pneumatic system is expressed as [ 28 , 29 ]:

where \(\dot{V}_{\text{s}}\) is the volume flow through the gas supply pipeline, and its numerical variation curves are shown in Figures  9 (b) and 10 (b). The \(T_{\text{s}}\) curves are shown in Figures  9 (c) and 10 (c).

The effective power of the pneumatic rotary actuator can be expressed as

where \(\dot{V}_{\text{a}}\) is the volume flow into Chamber a , and its numerical variation curves are shown in Figures  9 (e) and 10 (e). The \(T_{\text{a}}\) curves are shown in Figures  9 (f) and 10 (f).

By substituting the data in Figures  9 and 10 into Eqs. ( 17 ) and ( 18 ), the total and effective power of the pneumatic system at different supply pressure values can be obtained, as shown in Figure  11 . The total and effective energy consumed by the pneumatic system can be obtained by integrating the data shown in Figure  11 using the Origin software.

figure 11

Total and effective power of the pneumatic system under different supply pressure values: ( a ) Total power, ( b ) Effective power

The actual work done by the gas on the pneumatic rotary actuator is equal to the sum of the rotational kinetic energy of the rotating platform, the kinetic energy of the cylinder piston, and the work done by the piston to overcome the friction force, which can be expressed as

where \(y\) is the displacement of the actuator piston and \(\dot{\theta }\) is replaced by the average value of the angular velocity.

The calculation results are described as follows. When the gas supply pressure is 0.6 MPa, the total energy consumed by the system is 195.552 J, the effective energy is 32.666 J, and the actual work done by the pneumatic rotary actuator is 3.513 J. When the gas supply pressure is 0.3367 MPa, the total energy consumed by the system is 32.207 J, the effective energy is 9.481 J, and the actual work done is 3.517 J. In both cases, the actual work of the pneumatic rotary actuator is almost the same, and when the gas supply pressure is 0.3367 MPa, the energy consumption is greatly reduced.

5 Further Discussions

According to the matching method of the power characteristics, for the constant-pressure source servo system with a quantitative pump, we need to calculate the optimal air-supply pressure required for manually adjusting the air-supply pressure to the optimal pressure. Matching efficiency η represents the ratio of the power output of the pneumatic system to the input power of the gas source. The matching efficiency is expressed as

Figure  7 shows that the matching efficiency of this method is low. The adaptive power source can adaptively change the gas supply pressure or flow to meet the system requirements and improve the matching efficiency. It can be divided into the following three types [ 30 ].

Flow adaptive power source

This power source can adaptively adjust the supply flow from the power source according to the system flow demand to reduce the loss in the flow. The characteristic curve is shown in Figure  12 (a). The matching efficiency is expressed as

figure 12

Power characteristics of the adaptive power sources: ( a ) Flow adaptive power source, ( b ) Pressure adaptive power source, ( c ) Power adaptive power source

Pressure adaptive power source

This power source can adaptively adjust the gas supply pressure of the power source according to the system pressure demand to reduce the pressure loss. The characteristic curve is shown in Figure  12 (b). The matching efficiency is expressed as

Power adaptive power source

This power source can adaptively adjust the gas supply pressure and flow from the power source according to the system pressure and flow demand to minimize the loss in power. \(\dot{m}_{\text{s}}^{'}\) denotes the air-supply flow. The characteristic curve is shown in Figure  12 (c). The matching efficiency is expressed as

Therefore, the power adaptive power source demonstrates better energy-saving effect, and its matching efficiency is closer to 100%.

6 Conclusions

Power matching of the pneumatic rotary actuator involves optimizing the relevant parameters of the pneumatic rotary actuator system based on the premise of satisfying the normal operation of the pneumatic rotary actuator, realizing the power demand and output matching, and achieving energy savings. In this study, the derivation process of the output-power and load characteristics of the pneumatic rotary actuator servo-control system is described. The employed air compressor is regarded as a constant-pressure source of the quantitative pump, and the power characteristics of the system are matched. The following conclusions are obtained.

The minimum gas supply pressure obtained by the power-matching method represents the optimal gas supply pressure. The optimum gas supply pressure is 0.3367 MPa.

By comparing the system-response experiments at 0.6 and 0.3367 MPa, the total energy consumed by the system generates savings of 163.345 J. This value verifies that the system under the optimal gas supply pressure can significantly reduce energy loss.

According to the characteristic curves of the adaptive power sources, the matching efficiency of the power adaptive power source is higher than that of the flow and pressure adaptive power sources.

L Ge, L Quan, X G Zhang, et al. Power matching and energy efficiency improvement of hydraulic excavator driven with speed and displacement variable power source. Chinese Journal of Mechanical Engineering , 2019, 32:100, https://doi.org/10.1186/s10033-019-0415-x .

Article   Google Scholar  

T Chen, L Cai, X F Ma, et al. Modeling and matching performance of a hybrid-power gas engine heat pump system with continuously variable transmission. Building Simulation , 2019, 12(2): 273-283.

G W Jia, W Q Xu, M L Cai, et al. Micron-sized water spray-cooled quasi-isothermal compression for compressed air energy storage. Experimental Thermal and Fluid Science , 2018, 96: 470-481.

D Shaw, J-J Yu, C Chieh. Design of a hydraulic motor system driven by compressed air. Energies , 2013, 6(7): 3149-3166.

M Cheng, B Xu, J H Zhang, et al. Pump-based compensation for dynamic improvement of the electrohydraulic flow matching system. IEEE Transactions on Industrial Electronics , 2017, 64(4): 2903-2913.

Y M Zhang, K Li, G Wang, et al. Nonlinear model establishment and experimental verification of a pneumatic rotary actuator position servo system. Energies , 2019, 12(6): 1096.

T L Brown, V P Atluri, J P Schmiedeler. A low-cost hybrid drivetrain concept based on compressed air energy storage. Applied Energy , 2014, 134: 477-489.

Y M Zhang, M L Cai. Overall life cycle comprehensive assessment of pneumatic and electric actuator. Chinese Journal of Mechanical Engineering , 2014, 27(3): 584-594.

M L Cai. Energy saving technology on pneumatic systems. Chinese Hydraulics & Pneumatics , 2013(8): 1-8. (in Chinese)

Google Scholar  

J F Li. Energy saving of pneumatic system . Beijing: Machinery Industry Press, 1997. (in Chinese)

R Saidur, N A Rahim, M Hasanuzzaman. A review on compressed-air energy use and energy savings. Renewable and Sustainable Energy Reviews , 2010, 14(4): 1135-1153.

Y M Zhang, S Wang, S L Wei, et al. Optimization of control method of air compressor group under intermittent large flow condition. Fluid Machinery , 2017, 45(7): 7-11.

K Baghestan, S M Rezaei, H A Talebi, et al. An energy-saving nonlinear position control strategy for electro-hydraulic servo systems. ISA Trans. , 2015, 59: 268-279.

S P Yang, H Yu, J G Liu, et al. Research on power matching and energy saving control of power system in hydraulic excavator. Journal of Mechanical Engineering , 2014, 50(5): 152-160. (in Chinese)

M Cheng, B Xu, J H Zhang, et al. Valve-based compensation for controllability improvement of the energy-saving electrohydraulic flow matching system. Journal of Zhejiang University: Science A , 2017, 18(6): 430-442.

B Xu, M Cheng, H Y Yang, et al. A hybrid displacement/pressure control scheme for an electrohydraulic flow matching system. IEEE/ASME Transactions on Mechatronics , 2015, 20(6): 2771-2782.

W N Huang, L Quan, J H Huang, et al. Flow matching with combined control of the pump and the valves for the independent metering swing system of a hydraulic excavator. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering , 2018, 232(10): 1310-1322.

B Xu, M Cheng, H Y Yang, et al. Electrohydraulic flow matching system with bypass pressure compensation. Journal of Zhejiang University (Engineering Science) , 2015, 49(9): 1762-1767. (in Chinese)

Y Z Kan, D Y Sun, Y Luo, et al. Optimal design of power matching for wheel loader based on power reflux hydraulic transmission system. Mechanism and Machine Theory , 2019, 137: 67-82.

H Y Yang, W Liu, B Xu, et al. Characteristic analysis of electro-hydraulic flow matching control system in hydraulic excavator. Journal of Mechanical Engineering , 2012, 48(14): 156-163. (in Chinese)

X Guo, C Lu, J Li, et al. Analysis of motor-pump system power matching based on genetic algorithm. EEA - Electrotehnica, Electronica, Automatica , 2018, 66(1): 93-99.

X Wang, H Lv, Q Sun, et al. A proportional resonant control strategy for efficiency improvement in extended range electric vehicles. Energies , 2017, 10(2): 204.

X L Lai, C Guan. A parameter matching method of the parallel hydraulic hybrid excavator optimized with genetic algorithm. Mathematical Problems in Engineering , 2013: 1-6.

X D Yan, L Quan, J Yang. Analysis on steering characteristics of wheel loader based on electric-hydraulic flow matching principle. Transactions of the Chinese Society of Agricultural Engineering , 2015, 31(18): 71-78. (in Chinese)

L C Xu, X M Hou. Power matching on loader engine and hydraulic torque converter based on typical operating conditions. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering , 2015, 31(7): 80-84. (in Chinese)

X H Fu, M L Cai, W Y X ang, et al. Optimization study on expansion energy used air-powered vehicle with pneumatic-hydraulic transmission. Chinese Journal of Mechanical Engineering , 2018, 31:3, https://doi.org/10.1186/s10033-018-0220-y .

H B Yuan, H Na, Y Kim. Robust MPC–PIC force control for an electro-hydraulic servo system with pure compressive elastic load. Control Engineering Practice , 2018, 79: 170-184.

Y Shi, M L Cai, W Q Xu, et al. Methods to evaluate and measure power of pneumatic system and their applications. Chinese Journal of Mechanical Engineering , 2019, 32:42, https://doi.org/10.1186/s10033-019-0354-6 .

Y Shi, T C Wu, M L Cai, et al. Energy conversion characteristics of a hydropneumatic transformer in a sustainable-energy vehicle. Applied Energy , 2016, 171: 77-85.

C C Zhan, X Y Chen. Hydraulic reliability optimization and intelligent fault diagnosis . Beijing: Metallurgical Industry Press, 2015. (in Chinese)

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Acknowledgments

The authors would like to thank Henan Polytechnic University and Beihang University for providing the necessary facilities and machinery to build the prototype of the pneumatic servo system. The authors are sincerely grateful to the reviewers for their valuable review comments, which substantially improved the paper.

Supported by Henan Province Science and Technology Key Project of China (Grant Nos. 202102210081, 202102210082), Fundamental Research Funds for Henan Province Colleges and Universities of China (Grant No. NSFRF140120), and Doctor Foundation of Henan Polytechnic University (Grant No. B2012-101).

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Yeming Zhang & Hongwei Yue

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YZ provided guidance for the whole research. KL and HY established the model, designed the experiments and wrote the initial manuscript. KL and MC assisted with sampling and laboratory analyses. YZ and HY revised the manuscript, performed the experiments and analysed the data. All authors read and approved the final manuscript.

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Yeming Zhang, born in 1979, is currently is an associate professor at School of Mechanical and Power Engineering, Henan Polytechnic University, China . He received his PhD degree from Beihang University, China , in 2011. His research interests include complex mechatronics system design and simulation, intelligent control, reliability and fault diagnosis, pneumatic system energy saving and flow measurement.

Hongwei Yue, born in 1992, is currently a master candidate at School of Mechanical and Power Engineering, Henan Polytechnic University, China .

Ke Li, born in 1991, is currently a PhD candidate at School of Mechanical and Electrical Engineering, Harbin Institute of Technology, China . He received his master degree on mechano-electronic from Henan Polytechnic University, China , in 2019.

Maolin Cai, born in 1972, is currently a professor and a PhD candidate supervisor at Beihang University, China . He received his PhD degree from Tokyo Institute of Technology, Japan , in 2002. His main research direction includes pneumatic and hydraulic fluidics, compressed air energy storage, and pneumatic pipe line system.

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Zhang, Y., Yue, H., Li, K. et al. Analysis of Power Matching on Energy Savings of a Pneumatic Rotary Actuator Servo-Control System. Chin. J. Mech. Eng. 33 , 30 (2020). https://doi.org/10.1186/s10033-020-00445-3

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Published : 09 April 2020

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  • Pneumatic rotary actuator
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What is context in knowledge translation? Results of a systematic scoping review

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Knowledge Translation (KT) aims to convey novel ideas to relevant stakeholders, motivating their response or action to improve people’s health. Initially, the KT literature focused on evidence-based medicine, applying findings from laboratory and clinical research to disease diagnosis and treatment. Since the early 2000s, the scope of KT has expanded to include decision-making with health policy implications.

This systematic scoping review aims to assess the evolving knowledge-to-policy concepts, that is, macro-level KT theories, models and frameworks (KT TMFs). While significant attention has been devoted to transferring knowledge to healthcare settings (i.e. implementing health policies, programmes or measures at the meso-level), the definition of 'context' in the realm of health policymaking at the macro-level remains underexplored in the KT literature. This study aims to close the gap.

A total of 32 macro-level KT TMFs were identified, with only a limited subset of them offering detailed insights into contextual factors that matter in health policymaking. Notably, the majority of these studies prompt policy changes in low- and middle-income countries and received support from international organisations, the European Union, development agencies or philanthropic entities.

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Few concepts are used by health researchers as vaguely and yet as widely as Knowledge Translation (KT), a catch-all term that accommodates a broad spectrum of ambitions. Arguably, to truly understand the role of context in KT, we first need to clarify what KT means. The World Health Organization (WHO) defines KT as ‘the synthesis, exchange and application of knowledge by relevant stakeholders to accelerate the benefits of global and local innovation in strengthening health systems and improving people’s health’ [ 1 ]. Here, particular attention should be paid to ‘innovation’, given that without unpacking this term, the meaning of KT would still remain ambiguous. Rogers’ seminal work ‘Diffusion of Innovations’ [ 2 ] defines innovation as an idea, practice or object that is perceived as novel by individuals or groups adopting it. In this context, he argues that the objective novelty of an idea in terms of the amount of time passed after its discovery holds little significance [ 2 ]. Rather, it is the subjective perception of newness by the individual that shapes their response [ 2 ]. In other words, if an idea seems novel to individuals, and thereby relevant stakeholders according to the aforementioned WHO definition, it qualifies as an innovation. From this perspective, it can be stated that a fundamental activity of KT is to communicate ideas that could be perceived as original to the targeted stakeholders, with the aim of motivating their response to improve health outcomes. This leaves us with the question of who exactly these stakeholders might be and what kind of actions would be required from them.

The scope of stakeholders in KT has evolved over time, along with their prompted responses. Initially, during the early phases of KT, the focus primarily revolved around healthcare providers and their clinical decisions, emphasising evidence-based medicine. Nearly 50 years ago, the first scientific article on KT was published, introducing Tier 1 KT, which concentrated on applying laboratory discoveries to disease diagnosis or treatment, also known as bench-to-bedside KT [ 3 ]. The primary motivation behind this initial conceptualisation of KT was to engage healthcare providers as the end-users of specific forms of knowledge, primarily related to randomised controlled trials of pharmaceuticals and evidence-based medicine [ 4 ]. In the early 2000s, the second phase of KT (Tier 2) emerged under the term ‘campus-to-clinic KT’ [ 3 ]. This facet, also known as translational research, was concerned with using evidence from health services research in healthcare provision, both in practice and policy [ 4 ]. Consequently, by including decision-makers as relevant end-users, KT scholars expanded the realm of research-to-action from the clinical environment to policy-relevant decision-making [ 5 ]. Following this trajectory, additional KT schemes (Tier 3–Tier 5) have been introduced into academic discourse, encompassing the dissemination, implementation and broader integration of knowledge into public policies [ 6 , 7 ]. Notably, the latest scheme (Tier 5) is becoming increasingly popular and represents the broadest approach, which describes the translation of knowledge to global communities and aims to involve fundamental, universal change in attitudes, policies and social systems [ 7 ].

In other words, a noticeable shift in KT has occurred with time towards macro-level interventions, named initially as evidence- based policymaking and later corrected to evidence- informed policymaking. In parallel with these significant developments, various alternative terms to KT have emerged, including ‘implementation science’, ‘knowledge transfer’, and ‘dissemination and research use’, often with considerable overlap [ 8 ]. Arguably, among the plethora of alternative terms proposed, implementation science stands out prominently. While initially centred on evidence-based medicine at the meso-level (e.g. implementing medical guidelines), it has since broadened its focus to ‘encompass all aspects of research relevant to the scientific study of methods to promote the uptake of research findings into routine settings in clinical, community and policy contexts’ [ 9 ], closely mirroring the definition to KT. Thus, KT, along with activities under different names that share the same objective, has evolved into an umbrella term over the years, encompassing a wide range of strategies aimed at enhancing the impact of research not only on clinical practice but also on public policies [ 10 ]. Following the adoption of such a comprehensive definition of KT, some researchers have asserted that using evidence in public policies is not merely commendable but essential [ 11 ].

In alignment with the evolution of KT from (bio-)medical sciences to public policies, an increasing number of scholars have offered explanations on how health policies should be developed [ 12 ], indicating a growing focus on exploring the mechanisms of health policymaking in the KT literature. However, unlike in the earlier phases of KT, which aimed to transfer knowledge from the laboratory to healthcare provision, decisions made for public policies may be less technical and more complex than those in clinical settings [ 3 , 13 , 14 ]. Indeed, social scientists point out that scholarly works on evidence use in health policies exhibit theoretical shortcomings as they lack engagement with political science and public administration theories and concepts [ 15 , 16 , 17 , 18 ]; only a few of these works employ policy theories and political concepts to guide data collection and make sense of their findings [ 19 ]. Similarly, contemporary literature that conceptualises KT as an umbrella term for both clinical and public policy decision-making, with calls for a generic ‘research-to-action’ [ 20 ], may fail to recognise the different types of actions required to change clinical practices and influence health policies. In many respects, such calls can even lead to a misconception that evidence-informed policymaking is simply a scaled-up version of evidence-based medicine [ 21 ].

In this study, we systematically review knowledge translation theories, models and frameworks (also known as KT TMFs) that were developed for health policies. Essentially, KT TMFs can be depicted as bridges that connect findings across diverse studies, as they establish a common language and standardise the measurement and assessment of desired policy changes [ 22 ]. This makes them essential for generalising implementation efforts and research findings [ 23 ]. While distinctions between a theory, a model or a framework are not always crystal-clear [ 24 ], the following definitions shed light on how they are interpreted in the context of KT. To start with, theory can be described as a set of analytical principles or statements crafted to structure our observations, enhance our understanding and explain the world [ 24 ]. Within implementation science, theories are encapsulated as either generalised models or frameworks. In other words, they are integrated into broader concepts, allowing researchers to form assumptions that help clarify phenomena and create hypotheses for testing [ 25 ].

Whereas theories in the KT literature are explanatory as well as descriptive, KT models are only descriptive with a more narrowly defined scope of explanation [ 24 ]; hence they have a more specific focus than theories [ 25 ]. KT models are created to facilitate the formulation of specific assumptions regarding a set of parameters or variables, which can subsequently be tested against outcomes using predetermined methods [ 25 ]. By offering simplified representations of complex situations, KT models can describe programme elements expected to produce desired results, or theoretical constructs believed to influence or moderate observed outcomes. In this way, they encompass theories related to change or explanation [ 22 ].

Lastly, frameworks in the KT language define a set of variables and the relations among them in a broad sense [ 25 ]. Frameworks, without the aim of providing explanations, solely describe empirical phenomena, representing a structure, overview, outline, system or plan consisting of various descriptive categories and the relations between them that are presumed to account for a phenomenon [ 24 ]. They portray loosely-structured constellations of theoretical constructs, without necessarily specifying their relationships; they can also offer practical methods for achieving implementation objectives [ 22 ]. Some scholars suggest sub-classifications and categorise a framework as ‘actionable’ if it has the potential to facilitate macro-level policy changes [ 11 ].

Context, which encompasses the entire environment in which policy decisions are made, is not peripheral but central to policymaking, playing a crucial role in its conceptualisation [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. In the KT literature, the term ‘context’ is frequently employed, albeit often with a lack of precision [ 35 ]. It tends to serve as a broad term including various elements within a situation that are relevant to KT in some way but have not been explicitly identified [36]. However, there is a growing interest in delving deeper into what context refers to, as evidenced by increasing research attention [ 31 , 32 , 37 , 38 , 39 , 40 , 41 ]. While the definition of context in the transfer of knowledge to healthcare settings (i.e. implementing health policies, programmes or measures at the meso-level) has been systematically studied [ 36 , 37 , 42 , 43 ], the question of how KT scholars detail context in health policymaking remains unanswered. With our systematic scoping review, we aim to close this gap.

While KT TMFs, emerged from evidence-based medicine, have historically depicted the use of evidence from laboratories or healthcare organisations as the gold standard, we aimed to assess in this study whether and to what extent the evolving face of KT, addressing health policies, succeeded in foregrounding ‘context’. Our objective was thus not to evaluate the quality of these KT TMFs but rather to explore how scholars have incorporated contextual influences into their reasoning. We conducted a systematic scoping review to explore KT TMFs that are relevant to agenda-setting, policy formulation or policy adoption, in line with the aim of this study. Therefore, publications related to policy implementation in healthcare organisations or at the provincial level, as well as those addressing policy evaluation, did not meet our inclusion criteria. Consequently, given our focus on macro-level interventions, we excluded all articles that concentrate on translating clinical research into practice (meso-level interventions) and health knowledge to patients or citizens (micro-level interventions).

Prior systematic scoping reviews in the area of KT TMFs serve as a valuable foundation upon which to build further studies [ 44 , 45 ]. Using established methodologies may ensure a validated approach, allowing for a more nuanced understanding of KT TMFs in the context of existing scholarly work. Our review methodology employed a similar approach to that followed by Strifler et al. in 2018, who conducted a systematic scoping review of KT TMFs in the field of cancer prevention and management, as well as other chronic diseases [ 44 ]. Their search strategy was preferred over others for two primary reasons. First, Strifler et al. investigated KT TMFs altogether, systematically and comprehensively. Second, unlike many other review studies on KT, they focused on macro-level KT and included all relevant keywords useful for the purpose of our study in their Ovid/MEDLINE search query [ 44 ]. For our scoping review, we adapted their search query with the assistance of a specialist librarian. This process involved eliminating terms associated with cancer and chronic diseases, removing time limitation on the published papers, and including an additional language other than English due to authors’ proficiency in German. We included articles published in peer-reviewed journals until November 2022, excluding opinion papers, conference abstracts and study protocols, without any restriction on publication date or place. Our search query is presented in Table  1 .

Following a screening methodology similar to that employed by Votruba et al. [ 11 ], the first author conducted an initial screening of the titles and abstracts of 2918 unique citations. Full texts were selected and scrutinised if they appeared relevant to the topics of agenda-setting, policy formulation or policy adoption. Among these papers, the first author also identified those that conceptualised a KT TMF. Simultaneously, the last author independently screened 2918 titles and abstracts, randomly selecting 20% of them to identify studies related to macro-level KT. Regarding papers that conceptualised a KT TMF, all those initially selected by the first author underwent a thorough examination by the last author as well. In the papers reviewed by these two authors of this study, KT TMFs were typically presented as either Tables or Figures. In cases where these visual representations did not contain sufficient information about ‘context’, the main body of the study was carefully scrutinised by both reviewers to ensure no relevant information was missed. Any unclear cases were discussed and resolved to achieve 100% inter-rater agreement between the first and second reviewers. This strategy resulted in the inclusion of 32 relevant studies. The flow chart outlining our review process is provided in Fig.  1 .

figure 1

Flow chart of the review process

According to the results of our systematic scoping review (Table  2 ), the first KT TMF developed for health policies dates back to 2003, confirming the emergence of a trend that expanded the meaning of the term Knowledge Translation to include policymakers as end-users of evidence during approximately the same period. In their study, Jacobson et al. [ 46 ] present a framework derived from a literature review to enhance understanding of user groups by organising existing knowledge, identifying gaps and emphasising the importance of learning about new contexts. However, despite acknowledging the significance of the user group context, the paper lacks a thorough explanation of the authors’ understanding of this term. The second study in our scoping review provides some details. Recognising a shift from evidence-based medicine to evidence-based health policymaking in the KT literature, the article by Dobrow et al. from 2004 [ 30 ] emphasises the importance of considering contextual factors. They present a conceptual framework for evidence-based decision-making, highlighting the influence of context in KT. Illustrated through examples from colorectal cancer screening policy development, their conceptual framework emphasises the significance of context in the introduction, interpretation and application of evidence. Third, Lehoux et al. [ 47 ] examine the field of Health Technology Assessment (HTA) and its role in informing decision and policymaking in Canada. By developing a conceptual framework for HTA dissemination and use, they touch on the institutional environment and briefly describe contextual factors.

Notably, the first three publications in our scoping review are authored by scholars affiliated with Canada, which is less of a coincidence, given the role of Canadian Institutes of Health Research (CIHR), the federal funding agency for health research: The CIHR Act (Bill C-13) mandates CIHR to ensure that the translation of health knowledge permeates every aspect of its work [ 48 ]. Moreover, it was CIHR that coined the term Knowledge Translation, defining KT as ‘a dynamic and iterative process that includes the synthesis, dissemination, exchange and ethically sound application of knowledge to improve health, provide more effective health services and products, and strengthen the health care system’ [ 49 ] . This comprehensive definition has since been adapted by international organisations (IOs), including WHO. The first document published by WHO that utilised KT to influence health policies dates back to 2005, entitled ‘Bridging the “know-do” gap: Meeting on knowledge translation in global health’, an initiative that was supported by the Canadian Coalition for Global Health Research, the Canadian International Development Agency, the German Agency for Technical Cooperation and the WHO Special Programme on Research and Training in Tropical Diseases [ 1 ]. Following this official recognition by WHO, studies in our scoping review after 2005 indicate a noticeable expansion of KT, encompassing a wider geographical area than Canada.

The article of Ashford et al. from 2006 [ 50 ] discusses the challenge of policy decisions in Kenya in the health field being disconnected from scientific evidence and presents a model for translating knowledge into policy actions through agenda-setting, coalition building and policy learning. However, the framework lacks explicit incorporation of contextual factors influencing health policies. Bauman et al. [ 51 ] propose a six-step framework for successful dissemination of physical activity evidence, illustrated through four case studies from three countries (Canada, USA and Brazil) and a global perspective. They interpret contextual factors as barriers and facilitators to physical activity and public health innovations. Focusing on the USA, Gold [ 52 ] explains factors, processes and actors that shape pathways between research and its use in a summary diagram, including a reference to ‘other influences in process’ for context. Green et al. [ 4 ] examine the gap between health research and its application in public health without focusing on a specific geographical area. Their study comprehensively reviews various concepts of diffusion, dissemination and implementation in public health, proposing ways to blend diffusion theory with other theories. Their ‘utilization-focused surveillance framework’ interprets context as social determinants as structures, economics, politics and culture.

Further, the article by Dhonukshe-Rutten et al. from 2010 [ 53 ] presents a general framework that outlines the process of translating nutritional requirements into policy applications from a European perspective. The framework incorporates scientific evidence, stakeholder interests and the socio-political context. The description of this socio-political context is rather brief, encompassing political and social priorities, legal context, ethical issues and economic implications. Ir et al. [ 54 ] analyse the use of knowledge in shaping policy on health equity funds in Cambodia, with the objective of understanding how KT contributes to the development of health policies that promote equity. Yet no information on context is available in the framework that they suggest. A notable exception among these early KT TMFs until 2010 is the conceptual framework for analysing integration of targeted health interventions into health systems by Atun et al. [ 55 ], in which the authors provide details about the factors that have an influence on the process of bringing evidence to health policies. Focusing on the adoption, diffusion and assimilation of health interventions, their conceptual framework provides a systematic approach for evaluating and informing policies in this field. Compared to the previous studies discussed above, their definition of context for this framework is comprehensive (Table  2 ). Overall, most of the studies containing macro-level KT TMFs published until 2010 either do not fully acknowledge contextual factors or provide generic terms such as cultural, political and economic for brief description (9 out of 10; 90%).

Studies published after 2010 demonstrate a notable geographical shift, with a greater emphasis on low- and middle-income countries (LMICs). By taking the adoption of the directly observed treatment, short-course (DOTS) strategy for tuberculosis control in Mexico as a case study, Bissell et al. [ 56 ] examine policy transfer to Mexico and its relevance to operational research efforts and suggest a model for analysis of health policy transfer. The model interprets context as health system, including political, economic, social, cultural and technological features. Focusing on HIV/AIDS in India, Tran et al. [ 57 ] explore KT by considering various forms of evidence beyond scientific evidence, such as best practices derived from programme experience and disseminated through personal communication. Their proposed framework aims to offer an analytical tool for understanding how evidence-based influence is exerted. In their framework, no information is available on context. Next, Bertone et al. [ 58 ] report on the effectiveness of Communities of Practice (CoPs) in African countries and present a conceptual framework for analysing and assessing transnational CoPs in health policy. The framework organises the key elements of CoPs, linking available resources, knowledge management activities, policy and practice changes, and improvements in health outcomes. Context is only briefly included in this framework.

Some other studies include both European and global perspectives. The publication from Timotijevic et al. from 2013 [ 59 ] introduces an epistemological framework that examines the considerations influencing the policy-making process, with a specific focus on micronutrient requirements in Europe. They present case studies from several European countries, highlighting the relevance of the framework in understanding the policy context related to micronutrients. Context is interpreted in this framework as global trends, data, media, broader consumer beliefs, ethical considerations, and wider social, legal, political, and economic environment. Next, funded by the European Union, the study by Onwujekwe et al. [ 60 ] examines the role of different types of evidence in health policy development in Nigeria. Although they cover the factors related to policy actors in their framework for assessing the role of evidence in policy development, they provide no information on context. Moreover, Redman et al. [ 61 ] present the SPIRIT Action Framework, which aims to enhance the use of research in policymaking. Context is interpreted in this framework as policy influences, i.e. public opinion, media, economic climate, legislative/policy infrastructure, political ideology and priorities, stakeholder interests, expert advice, and resources. From a global perspective, Spicer et al. [ 62 ] explore the contextual factors that influenced the scale-up of donor-funded maternal and newborn health innovations in Ethiopia, India and Nigeria, highlighting the importance of context in assessing and adapting innovations. Their suggested contextual factors influencing government decisions to accept, adopt and finance innovations at scale are relatively comprehensive (Table  2 ).

In terms of publication frequency, the pinnacle of reviewed KT studies was in 2017. Among six studies published in 2017, four lack details about context in their KT conceptualisations and one study touches on context very briefly. Bragge et al. [ 5 ] brought for their study an international terminology working group together to develop a simplified framework of interventions to integrate evidence into health practices, systems, and policies, named as the Aims, Ingredients, Mechanism, Delivery framework, albeit without providing details on contextual factors. Second, Mulvale et al. [ 63 ] present a conceptual framework that explores the impact of policy dialogues on policy development, illustrating how these dialogues can influence different stages of the policy cycle. Similar to the previous one, this study too, lacks information on context. In a systematic review, Sarkies et al. [ 64 ] evaluate the effectiveness of research implementation strategies in promoting evidence-informed policy decisions in healthcare. The study explores the factors associated with effective strategies and their inter-relationship, yet without further information on context. Fourth, Houngbo et al. [ 65 ] focus on the development of a strategy to implement a good governance model for health technology management in the public health sector, drawing from their experience in Benin. They outline a six-phase model that includes preparatory analysis, stakeholder identification and problem analysis, shared analysis and visioning, development of policy instruments for pilot testing, policy development and validation, and policy implementation and evaluation. They provide no information about context in their model. Fifth, Mwendera et al. [ 66 ] present a framework for improving the use of malaria research in policy development in Malawi, which was developed based on case studies exploring the policymaking process, the use of local malaria research, and assessing facilitators and barriers to research utilisation. Contextual setting is considered as Ministry of Health (MoH) with political set up, leadership system within the MoH, government policies and cultural set up. In contrast to these five studies, Ellen et al. [ 67 ] present a relatively comprehensive framework to support evidence-informed policymaking in ageing and health. The framework includes thought-provoking questions to discover contextual factors (Table  2 ).

Continuing the trend, studies published after 2017 focus increasingly on LMICs. In their embedded case study, Ongolo-Zogo et al. [ 68 ] examine the influence of two Knowledge Translation Platforms (KTPs) on policy decisions to achieve the health millennium development goals in Cameroon and Uganda. It explores how these KTPs influenced policy through interactions within policy issue networks, engagement with interest groups, and the promotion of evidence-supported ideas, ultimately shaping the overall policy climate for evidence-informed health system policymaking. Contextual factors are thereby interpreted as institutions (structures, legacies, policy networks), interests, ideas (values, research evidence) and external factors (reports, commitments). Focusing on the ‘Global South’, Plamondon et al. [ 69 ] suggest blending integrated knowledge translation with global health governance as an approach for strengthening leadership for health equity action. In terms of contextual factors, they include some information such as adapting knowledge to local context, consideration of the composition of non-traditional actors, such as civil society and private sector, in governance bodies and guidance for meaningful engagement between actors, particularly in shared governance models. Further, Vincenten et al. [ 70 ] propose a conceptual model to enhance understanding of interlinking factors that influence the evidence implementation process. Their evidence implementation model for public health systems refers to ‘context setting’, albeit without providing further detail.

Similarly, the study by Motani et al. from 2019 [ 71 ] assesses the outcomes and lessons learned from the EVIDENT partnership that focused on knowledge management for evidence-informed decision-making in nutrition and health in Africa. Although they mention ‘contextualising evidence’ in their conceptual framework, information about context is lacking. Focusing on Latin America and the Caribbean, Varallyay et al. [ 72 ] introduce a conceptual framework for evaluating embedded implementation research in various contexts. The framework outlines key stages of evidence-informed decision-making and provides guidance on assessing embeddedness and critical contextual factors. Compared to others, their conceptual framework provides a relatively comprehensive elaboration on contextual factors. In addition, among all the studies reviewed, Leonard et al. [ 73 ] present an exceptionally comprehensive analysis, where they identify the facilitators and barriers to the sustainable implementation of evidence-based health innovations in LMICs. Through a systematic literature review, they scrutinise 79 studies and categorise the identified barriers and facilitators into seven groups: context, innovation, relations and networks, institutions, knowledge, actors, and resources. The first one, context, contains rich information that could be seen in Table  2 .

Continuing from LMICs, Votruba et al. [ 74 ] present in their study the EVITA (EVIdence To Agenda setting) conceptual framework for mental health research-policy interrelationships in LMICs with some information about context, detailed as external influences and political context. In a follow-up study, they offer an updated framework for understanding evidence-based mental health policy agenda-setting [ 75 ]. In their revised framework, context is interpreted as external context and policy sphere, encompassing policy agenda, window of opportunity, political will and key individuals. Lastly, to develop a comprehensive monitoring and evaluation framework for evidence-to-policy networks, Kuchenmüller et al. [ 76 ] present the EVIPNet Europe Theory of Change and interpret contextual factors for evidence-informed policymaking as political, economic, logistic and administrative. Overall, it can be concluded that studies presenting macro-level KT TMFs from 2011 until 2022 focus mainly on LMICs (15 out of 22; close to 70%) and the majority of them were funded by international (development) organisations, the European Commission and global health donor agencies. An overwhelming number of studies among them (19 out of 22; close to 90%) provide either no information on contextual details or these were included only partly with some generic terms in KT TMFs.

Our systematic scoping review suggests that the approach of KT, which has evolved from evidence-based medicine to evidence-informed policymaking, tends to remain closely tied to its clinical origins when developing TMFs. In other words, macro-level KT TMFs place greater emphasis on the (public) health issue at hand rather than considering the broader decision-making context, a viewpoint shared by other scholars as well [ 30 ]. One reason could be that in the early stages of KT TMFs, the emphasis primarily focused on implementing evidence-based practices within clinical settings. At that time, the spotlight was mostly on content, including aspects like clinical studies, checklists and guidelines serving as the evidence base. In those meso-level KT TMFs, a detailed description of context, i.e. the overall environment in which these practices should be implemented, might have been deemed less necessary, given that healthcare organisations, such as hospitals to implement medical guidelines or surgical safety checklists, show similar characteristics globally.

However, as the scope of KT TMFs continues to expand to include the influence on health policies, a deeper understanding of context-specific factors within different jurisdictions and the dynamics of the policy process is becoming increasingly crucial. This is even more important for KT scholars aiming to conceptualise large-scale changes, as described in KT Tier 5, which necessitate a thorough understanding of targeted behaviours within societies. As the complexity of interventions increases due to the growing number of stakeholders either affecting or being affected by them, the interventions are surrounded by a more intricate web of attitudes, incentives, relationships, rules of engagement and spheres of influence [ 7 ]. The persisting emphasis on content over context in the evolving field of KT may oversimplify the complex process of using evidence in policymaking and understanding the society [ 77 ]. Some scholars argue that this common observation in public health can be attributed to the dominance of experts primarily from medical sciences [ 78 , 79 , 80 ]. Our study confirms the potential limitation of not incorporating insights from political science and public policy studies, which can lead to what is often termed a ‘naïve’ conceptualisation of evidence-to-policy schemes [ 15 , 16 , 17 ]. It is therefore strongly encouraged that the emerging macro-level KT concepts draw on political science and public administration if KT scholars intend to effectively communicate new ideas to policymakers, with the aim of prompting their action or response. We summarised our findings into three points.

Firstly, KT scholars may want to identify and pinpoint exactly where a change should occur within the policy process. The main confusion that we observed in the KT literature arises from a lack of understanding of how public policies are made. Notably, the term ‘evidence-informed policymaking’ can refer to any stage of the policy cycle, spanning from agenda-setting to policy formulation, adoption, implementation and evaluation. Understanding these steps will allow researchers to refine their language when advocating for policy changes across various jurisdictions; for instance, the word ‘implementation’ is often inappropriately used in KT literature. As commonly known, at the macro-level, public policies take the form of legislation, law-making and regulation, thereby shaping the practices or policies to be implemented at the meso- and micro-levels [ 81 ]. In other words, the process of using specific knowledge to influence health policies, however evidence-based it might be, falls mostly under the responsibility and jurisdiction of sovereign states. For this reason, macro-level KT TMFs should reflect the importance of understanding the policy context and the complexities associated with policymaking, rather than suggesting flawed or unrealistic top-down ‘implementation’ strategies in countries by foregrounding the content, or the (public) health issue at hand.

Our second observation from this systematic scoping review points towards a selective perception among researchers when reporting on policy interventions. Research on KT does not solely exist due to the perceived gap between scientific evidence and policy but also because of the pressures the organisations or researchers face in being accountable to their funding sources, ensuring the continuity of financial support for their activities and claiming output legitimacy to change public policies [ 8 ]. This situation indirectly compels researchers working to influence health policies in the field to provide ‘evidence-based’ feedback on the success of their projects to donors [ 82 ]. In doing so, researchers may overly emphasise the content of the policy intervention in their reporting to secure further funding, while they underemphasis the contextual factors. These factors, often perceived as a given, might actually be the primary facilitators of their success. Such a lack of transparency regarding the definition of context is particularly visible in the field of global health, where LMICs often rely on external donors. It is important to note that this statement is not intended as a negative critique of their missions or an evaluation of health outcomes in countries following such missions. Rather, it seeks to explain the underlying reason why researchers, particularly those reliant on donors in LMICs, prioritise promoting the concept of KT from a technical standpoint, giving less attention to contextual factors in their reasoning.

Lastly, and connected to the previous point, it is our observation that the majority of macro-level KT TMFs fail to give adequate consideration to both power dynamics in countries (internal vs. external influences) and the actual role that government plays in public policies. Notably, although good policymaking entails an honest effort to use the best available evidence, the belief that this will completely negate the role of power and politics in decision-making is a technocratic illusion [ 83 ]. Among the studies reviewed, the framework put forth by Leonard et al. [ 73 ] offers the most comprehensive understanding of context and includes a broad range of factors (such as political, social, and economic) discovered also in other reviewed studies. Moreover, the framework, developed through an extensive systematic review, offers a more in-depth exploration of these contextual factors than merely listing them as a set of keywords. Indeed, within the domains of political science and public policy, such factors shaping health policies have received considerable scholarly attention for decades. To define what context entails, Walt refers in her book ‘Health Policy: An Introduction to Process and Power’ [ 84 ] to the work of Leichter from 1979 [ 85 ], who provides a scheme for analysing public policy. This includes i) situational factors, which are transient, impermanent, or idiosyncratic; ii) structural factors, which are relatively unchanging elements of the society and polity; iii) cultural factors, which are value commitments of groups; and iv) environmental factors, which are events, structures and values that exist outside the boundaries of a political system and influence decisions within it. His detailed sub-categories for context can be found in Table  3 . This flexible public policy framework may offer KT researchers a valuable approach to understanding contextual factors and provide some guidance to define the keywords to focus on. Scholars can adapt this framework to suit a wide range of KT topics, creating more context-sensitive and comprehensive KT TMFs.

Admittedly, our study has certain limitations. Despite choosing one of the most comprehensive bibliographic databases for our systematic scoping review, which includes materials from biomedicine, allied health fields, biological and physical sciences, humanities, and information science in relation to medicine and healthcare, we acknowledge that we may have missed relevant articles indexed in other databases. Hence, exclusively using Ovid/MEDLINE due to resource constraints may have narrowed the scope and diversity of scholarly literature examined in this study. Second, our review was limited to peer-reviewed publications in English and German. Future studies could extend our findings by examining the extent to which contextual factors are detailed in macro-level KT TMFs published in grey literature and in different languages. Given the abundance of KT reports, working papers or policy briefs published by IOs and development agencies, such an endeavour could enrich our findings and either support or challenge our conclusions. Nonetheless, to our knowledge, this study represents the first systematic review and critical appraisal of emerging knowledge-to-policy concepts, also known as macro-level KT TMFs. It successfully blends insights from both biomedical and public policy disciplines, and could serve as a roadmap for future research.

The translation of knowledge to policymakers involves more than technical skills commonly associated with (bio-)medical sciences, such as creating evidence-based guidelines or clinical checklists. Instead, evidence-informed policymaking reflects an ambition to engage in the political dimensions of states. Therefore, the evolving KT concepts addressing health policies should be seen as a political decision-making process, rather than a purely analytical one, as is the case with evidence-based medicine. To better understand the influence of power dynamics and governance structures in policymaking, we suggest that future macro-level KT TMFs draw on insights from political science and public administration. Collaborative, interdisciplinary research initiatives could be undertaken to bridge the gap between these fields. Technocratic KT TMFs that overlook contextual factors risk propagating misconceptions in academic circles about how health policies are made, as they become increasingly influential over time. Research, the systematic pursuit of knowledge, is neither inherently good nor bad; it can be sought after, used or misused, like any other tool in policymaking. What is needed in the KT discourse is not another generic call for ‘research-to-action’ but rather an understanding of the dividing line between research-to- clinical -action and research-to- political -action.

Availability of data and materials

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WHO. Bridging the ‘Know-Do’ Gap: Meeting on Knowledge Translation in Global Health : 10–12 October 2005 World Health Organization Geneva, Switzerland [Internet]. 2005. https://www.measureevaluation.org/resources/training/capacity-building-resources/high-impact-research-training-curricula/bridging-the-know-do-gap.pdf

Rogers EM. Diffusion of innovations. 3rd ed. New York: Free Press; 1983.

Google Scholar  

Greenhalgh T, Wieringa S. Is it time to drop the ‘knowledge translation’ metaphor? A critical literature review. J R Soc Med. 2011;104(12):501–9.

Article   PubMed   PubMed Central   Google Scholar  

Green LW, Ottoson JM, García C, Hiatt RA. Diffusion theory and knowledge dissemination, utilization, and integration in public health. Annu Rev Public Health. 2009;30(1):151–74.

Article   PubMed   Google Scholar  

Bragge P, Grimshaw JM, Lokker C, Colquhoun H, Albrecht L, Baron J, et al. AIMD—a validated, simplified framework of interventions to promote and integrate evidence into health practices, systems, and policies. BMC Med Res Methodol. 2017;17(1):38.

Zarbin M. What Constitutes Translational Research? Implications for the Scope of Translational Vision Science and Technology. Transl Vis Sci Technol 2020;9(8).

Hassmiller Lich K, Frerichs L, Fishbein D, Bobashev G, Pentz MA. Translating research into prevention of high-risk behaviors in the presence of complex systems: definitions and systems frameworks. Transl Behav Med. 2016;6(1):17–31.

Tetroe JM, Graham ID, Foy R, Robinson N, Eccles MP, Wensing M, et al. Health research funding agencies’ support and promotion of knowledge translation: an international study. Milbank Q. 2008;86(1):125–55.

Eccles MP, Mittman BS. Welcome to Implementation Science. Implement Sci. 2006;1(1):1.

Article   PubMed Central   Google Scholar  

Rychetnik L, Bauman A, Laws R, King L, Rissel C, Nutbeam D, et al. Translating research for evidence-based public health: key concepts and future directions. J Epidemiol Community Health. 2012;66(12):1187–92.

Votruba N, Ziemann A, Grant J, Thornicroft G. A systematic review of frameworks for the interrelationships of mental health evidence and policy in low- and middle-income countries. Health Res Policy Syst. 2018;16(1):85.

Delnord M, Tille F, Abboud LA, Ivankovic D, Van Oyen H. How can we monitor the impact of national health information systems? Results from a scoping review. Eur J Public Health. 2020;30(4):648–59.

Malterud K, Bjelland AK, Elvbakken KT. Evidence-based medicine—an appropriate tool for evidence-based health policy? A case study from Norway. Health Res Policy Syst. 2016;14(1):15.

Borst RAJ, Kok MO, O’Shea AJ, Pokhrel S, Jones TH, Boaz A. Envisioning and shaping translation of knowledge into action: a comparative case-study of stakeholder engagement in the development of a European tobacco control tool. Health Policy. 2019;123(10):917–23.

Liverani M, Hawkins B, Parkhurst JO. Political and institutional influences on the use of evidence in public health policy: a systematic review. PLoS ONE. 2013;8(10): e77404.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cairney P. The politics of evidence-based policy making, 1st ed. London: Palgrave Macmillan UK: Imprint: Palgrave Pivot, Palgrave Macmillan; 2016.

Parkhurst J. The Politics of Evidence: From evidence-based policy to the good governance of evidence [Internet]. Routledge; 2016. https://www.taylorfrancis.com/books/9781315675008

Cairney P, Oliver K. Evidence-based policymaking is not like evidence-based medicine, so how far should you go to bridge the divide between evidence and policy? Health Res Policy Syst. 2017;15(1):35.

Verboom B, Baumann A. Mapping the Qualitative Evidence Base on the Use of Research Evidence in Health Policy-Making: A Systematic Review. Int J Health Policy Manag. 2020;16.

Ward V, House A, Hamer S. Developing a framework for transferring knowledge into action: a thematic analysis of the literature. J Health Serv Res Policy. 2009;14(3):156–64.

Swinburn B, Gill T, Kumanyika S. Obesity prevention: a proposed framework for translating evidence into action. Obes Rev. 2005;6(1):23–33.

Article   CAS   PubMed   Google Scholar  

Damschroder LJ. Clarity out of chaos: Use of theory in implementation research. Psychiatry Res. 2020;283: 112461.

Birken SA, Rohweder CL, Powell BJ, Shea CM, Scott J, Leeman J, et al. T-CaST: an implementation theory comparison and selection tool. Implement Sci. 2018;13(1):143.

Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. 2015;10(1):53.

Rapport F, Clay-Williams R, Churruca K, Shih P, Hogden A, Braithwaite J. The struggle of translating science into action: foundational concepts of implementation science. J Eval Clin Pract. 2018;24(1):117–26.

Hagenaars LL, Jeurissen PPT, Klazinga NS. The taxation of unhealthy energy-dense foods (EDFs) and sugar-sweetened beverages (SSBs): An overview of patterns observed in the policy content and policy context of 13 case studies. Health Policy. 2017;121(8):887–94.

Sheikh K, Gilson L, Agyepong IA, Hanson K, Ssengooba F, Bennett S. Building the field of health policy and systems research: framing the questions. PLOS Med. 2011;8(8): e1001073.

Tran NT, Hyder AA, Kulanthayan S, Singh S, Umar RSR. Engaging policy makers in road safety research in Malaysia: a theoretical and contextual analysis. Health Policy. 2009;90(1):58–65.

Walt G, Gilson L. Reforming the health sector in developing countries: the central role of policy analysis. Health Policy Plan. 1994;9(4):353–70.

Dobrow MJ, Goel V, Upshur REG. Evidence-based health policy: context and utilisation. Soc Sci Med. 2004;58(1):207–17.

Barnfield A, Savolainen N, Lounamaa A. Health Promotion Interventions: Lessons from the Transfer of Good Practices in CHRODIS-PLUS. Int J Environ Res Public Health. 2020;17(4).

van de Goor I, Hämäläinen RM, Syed A, Juel Lau C, Sandu P, Spitters H, et al. Determinants of evidence use in public health policy making: results from a study across six EU countries. Health Policy Amst Neth. 2017;121(3):273–81.

Article   Google Scholar  

Ornstein JT, Hammond RA, Padek M, Mazzucca S, Brownson RC. Rugged landscapes: complexity and implementation science. Implement Sci. 2020;15(1):85.

Seward N, Hanlon C, Hinrichs-Kraples S, Lund C, Murdoch J, Taylor Salisbury T, et al. A guide to systems-level, participatory, theory-informed implementation research in global health. BMJ Glob Health. 2021;6(12): e005365.

Pfadenhauer LM, Gerhardus A, Mozygemba K, Lysdahl KB, Booth A, Hofmann B, et al. Making sense of complexity in context and implementation: the Context and Implementation of Complex Interventions (CICI) framework. Implement Sci. 2017;12(1):21.

Rogers L, De Brún A, McAuliffe E. Defining and assessing context in healthcare implementation studies: a systematic review. BMC Health Serv Res. 2020;20(1):591.

Nilsen P, Bernhardsson S. Context matters in implementation science: a scoping review of determinant frameworks that describe contextual determinants for implementation outcomes. BMC Health Serv Res. 2019;19(1):189.

Arksey H, O’Malley L, Baldwin S, Harris J, Mason A, Golder S. Literature review report: services to support carers of people with mental health problems. 2002;182.

Tabak RG, Khoong EC, Chambers D, Brownson RC. Bridging research and practice. Am J Prev Med. 2012;43(3):337–50.

O’Donovan MA, McCallion P, McCarron M, Lynch L, Mannan H, Byrne E. A narrative synthesis scoping review of life course domains within health service utilisation frameworks. HRB Open Res. 2019.

Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A, et al. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care. 2005;14(1):26–33.

Bate P, Robert G, Fulop N, Øvretviet J, Dixon-Woods M. Perspectives on context: a collection of essays considering the role of context in successful quality improvement [Internet]. 2014. https://www.health.org.uk/sites/default/files/PerspectivesOnContext_fullversion.pdf

Ziemann A, Brown L, Sadler E, Ocloo J, Boaz A, Sandall J. Influence of external contextual factors on the implementation of health and social care interventions into practice within or across countries—a protocol for a ‘best fit’ framework synthesis. Syst Rev. 2019. https://doi.org/10.1186/s13643-019-1180-8 .

Strifler L, Cardoso R, McGowan J, Cogo E, Nincic V, Khan PA, et al. Scoping review identifies significant number of knowledge translation theories, models, and frameworks with limited use. J Clin Epidemiol. 2018;100:92–102.

Esmail R, Hanson HM, Holroyd-Leduc J, Brown S, Strifler L, Straus SE, et al. A scoping review of full-spectrum knowledge translation theories, models, and frameworks. Implement Sci. 2020;15(1):11.

Jacobson N, Butterill D, Goering P. Development of a framework for knowledge translation: understanding user context. J Health Serv Res Policy. 2003;8(2):94–9.

Lehoux P, Denis JL, Tailliez S, Hivon M. Dissemination of health technology assessments: identifying the visions guiding an evolving policy innovation in Canada. J Health Polit Policy Law. 2005;30(4):603–42.

Parliament of Canada. Government Bill (House of Commons) C-13 (36–2) - Royal Assent - Canadian Institutes of Health Research Act [Internet]. https://parl.ca/DocumentViewer/en/36-2/bill/C-13/royal-assent/page-31 . Accessed 1 Apr 2023.

Straus SE, Tetroe J, Graham I. Defining knowledge translation. CMAJ Can Med Assoc J. 2009;181(3–4):165–8.

Ashford L. Creating windows of opportunity for policy change: incorporating evidence into decentralized planning in Kenya. Bull World Health Organ. 2006;84(8):669–72.

Bauman AE, Nelson DE, Pratt M, Matsudo V, Schoeppe S. Dissemination of physical activity evidence, programs, policies, and surveillance in the international public health arena. Am J Prev Med. 2006;31(4):57–65.

Gold M. Pathways to the use of health services research in policy. Health Serv Res. 2009;44(4):1111–36.

Dhonukshe-Rutten RAM, Timotijevic L, Cavelaars AEJM, Raats MM, de Wit LS, Doets EL, et al. European micronutrient recommendations aligned: a general framework developed by EURRECA. Eur J Clin Nutr. 2010;64(2):S2-10.

Ir P, Bigdeli M, Meessen B, Van Damme W. Translating knowledge into policy and action to promote health equity: The Health Equity Fund policy process in Cambodia 2000–2008. Health Policy. 2010;96(3):200–9.

Atun R, de Jongh T, Secci F, Ohiri K, Adeyi O. Integration of targeted health interventions into health systems: a conceptual framework for analysis. Health Policy Plan. 2010;25(2):104–11.

Bissell K, Lee K, Freeman R. Analysing policy transfer: perspectives for operational research. Int J Tuberc Lung Dis Off J Int Union Tuberc Lung Dis. 2011;15(9).

Tran NT, Bennett SC, Bishnu R, Singh S. Analyzing the sources and nature of influence: how the Avahan program used evidence to influence HIV/AIDS prevention policy in India. Implement Sci. 2013;8(1):44.

Bertone MP, Meessen B, Clarysse G, Hercot D, Kelley A, Kafando Y, et al. Assessing communities of practice in health policy: a conceptual framework as a first step towards empirical research. Health Res Policy Syst. 2013;11(1):39.

Timotijevic L, Brown KA, Lähteenmäki L, de Wit L, Sonne AM, Ruprich J, et al. EURRECA—a framework for considering evidence in public health nutrition policy development. Crit Rev Food Sci Nutr. 2013;53(10):1124–34.

Onwujekwe O, Uguru N, Russo G, Etiaba E, Mbachu C, Mirzoev T, et al. Role and use of evidence in policymaking: an analysis of case studies from the health sector in Nigeria. Health Res Policy Syst. 2015;13(1):46.

Redman S, Turner T, Davies H, Williamson A, Haynes A, Brennan S, et al. The SPIRIT action framework: a structured approach to selecting and testing strategies to increase the use of research in policy. Soc Sci Med. 2015;136–137:147–55.

Spicer N, Berhanu D, Bhattacharya D, Tilley-Gyado RD, Gautham M, Schellenberg J, et al. ‘The stars seem aligned’: a qualitative study to understand the effects of context on scale-up of maternal and newborn health innovations in Ethiopia, India and Nigeria. Glob Health. 2016;12(1):75.

Mulvale G, McRae SA, Milicic S. Teasing apart “the tangled web” of influence of policy dialogues: lessons from a case study of dialogues about healthcare reform options for Canada. Implement Sci IS. 2017;12.

Sarkies MN, Bowles KA, Skinner EH, Haas R, Lane H, Haines TP. The effectiveness of research implementation strategies for promoting evidence-informed policy and management decisions in healthcare: a systematic review. Implement Sci. 2017;12(1):132.

Houngbo PTh, Coleman HLS, Zweekhorst M, De Cock Buning TJ, Medenou D, Bunders JFG. A Model for Good Governance of Healthcare Technology Management in the Public Sector: Learning from Evidence-Informed Policy Development and Implementation in Benin. PLoS ONE. 2017;12(1):e0168842.

Mwendera C, de Jager C, Longwe H, Hongoro C, Phiri K, Mutero CM. Development of a framework to improve the utilisation of malaria research for policy development in Malawi. Health Res Policy Syst. 2017;15(1):97.

Ellen ME, Panisset U, de AraujoCarvalho I, Goodwin J, Beard J. A knowledge translation framework on ageing and health. Health Policy. 2017;121(3):282–91.

Ongolo-Zogo P, Lavis JN, Tomson G, Sewankambo NK. Assessing the influence of knowledge translation platforms on health system policy processes to achieve the health millennium development goals in Cameroon and Uganda: a comparative case study. Health Policy Plan. 2018;33(4):539–54.

Plamondon KM, Pemberton J. Blending integrated knowledge translation with global health governance: an approach for advancing action on a wicked problem. Health Res Policy Syst. 2019;17(1):24.

Vincenten J, MacKay JM, Schröder-Bäck P, Schloemer T, Brand H. Factors influencing implementation of evidence-based interventions in public health systems—a model. Cent Eur J Public Health. 2019;27(3):198–203.

Motani P, Van de Walle A, Aryeetey R, Verstraeten R. Lessons learned from Evidence-Informed Decision-Making in Nutrition & Health (EVIDENT) in Africa: a project evaluation. Health Res Policy Syst. 2019;17(1):12.

Varallyay NI, Langlois EV, Tran N, Elias V, Reveiz L. Health system decision-makers at the helm of implementation research: development of a framework to evaluate the processes and effectiveness of embedded approaches. Health Res Policy Syst. 2020;18(1):64.

Leonard E, de Kock I, Bam W. Barriers and facilitators to implementing evidence-based health innovations in low- and middle-income countries: a systematic literature review. Eval Program Plann. 2020;82: 101832.

Votruba N, Grant J, Thornicroft G. The EVITA framework for evidence-based mental health policy agenda setting in low- and middle-income countries. Health Policy Plan. 2020;35(4):424–39.

Votruba N, Grant J, Thornicroft G. EVITA 2.0, an updated framework for understanding evidence-based mental health policy agenda-setting: tested and informed by key informant interviews in a multilevel comparative case study. Health Res Policy Syst. 2021;19(1):35.

Kuchenmüller T, Chapman E, Takahashi R, Lester L, Reinap M, Ellen M, et al. A comprehensive monitoring and evaluation framework for evidence to policy networks. Eval Program Plann. 2022;91: 102053.

Ettelt S. The politics of evidence use in health policy making in Germany—the case of regulating hospital minimum volumes. J Health Polit Policy Law. 2017;42(3):513–38.

Greer SL, Bekker M, de Leeuw E, Wismar M, Helderman JK, Ribeiro S, et al. Policy, politics and public health. Eur J Public Health. 2017;27(suppl 4):40–3.

Fafard P, Cassola A. Public health and political science: challenges and opportunities for a productive partnership. Public Health. 2020;186:107–9.

Löblová O. Epistemic communities and experts in health policy-making. Eur J Public Health. 2018;28(suppl 3):7–10.

Maddalena V. Evidence-Based Decision-Making 8: Health Policy, a Primer for Researchers. In: Parfrey PS, Barrett BJ, editors. Clinical Epidemiology: Practice and Methods. New York, NY: Springer; 2015. (Methods in Molecular Biology).

Louis M, Maertens L. Why international organizations hate politics - Depoliticizing the world [Internet]. London and New York: Routledge; 2021. (Global Institutions). https://library.oapen.org/bitstream/handle/20.500.12657/47578/1/9780429883279.pdf

Hassel A, Wegrich K. How to do public policy. 1st ed. Oxford: Oxford University Press; 2022.

Book   Google Scholar  

Walt G. Health policy: an introduction to process and power. 7th ed. Johannesburg: Witwatersrand University Press; 2004.

Leichter HM. A comparative approach to policy analysis: health care policy in four nations. Cambridge: Cambridge University Press; 1979.

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Schmitt, T., Czabanowska, K. & Schröder-Bäck, P. What is context in knowledge translation? Results of a systematic scoping review. Health Res Policy Sys 22 , 52 (2024). https://doi.org/10.1186/s12961-024-01143-5

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Medium-duty and heavy-duty commercial vehicles (with a total vehicle weight of 7.5 or more) are equipped with pneumatic brake systems. If the medium of compressed air is already available, it makes sense to use it for other functions as well. The components that generate the compressed air for these systems are explained.

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Historically, pneumatic brakes were first developed as a technology for railways. In railway applications, the coupling problem is many times more significant, because many carriages are coupled one behind the other.

There are also designs with compressors that can be switched off. A coupling between the air compressor crankshaft and the diesel engine drive unit can be used to shut down the compressor completely. This reduces the vehicle’s power draw and fuel consumption. Switching off the air compressor can also be expected to prolong the service life of the compressor. However, this advantage must be weighed against the additional costs and extra weight of the coupling as well as the addition of another component, namely the coupling.

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Hilgers, M., Achenbach, W. (2021). Pneumatics System. In: Electrical Systems and Mechatronics. Commercial Vehicle Technology. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-60838-8_2

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