Nonlinear controller design and testing for chatter suppression in an electric-pneumatic braking system with parametric variation

Wu, Xiuheng; Zhang, Lipeng; Wang, Xiangyu; Chen, Xiang; Cheng, Shuo

doi:10.1016/j.ymssp.2019.106401

Cited by 19 publications

(6 citation statements)

References 32 publications

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“…For this kind of system with switching valve and strong nonlinearity, many significant works regarding PEBS nonlinearity analysis and modeling are available and based on these many methods are proposed to improve closed-loop performance. Among them, the reference [11][12][13][14] proposed to establish the dynamic model of the system based on the experimental data and carried on the application control.…”

Section: Introductionmentioning

confidence: 99%

“…For this kind of system with switching valves, many researchers proposed to establish the dynamic model of the system based on the experimental data and carried on the application control. [11][12][13][14][15][16] Palanivelu et al 17 proposed an adaptive modeling method, after modeling the parts in the system, it is convenient to simulate the system and optimize the parameters to obtain the ideal braking performance. Kong et al 18 studied the stress process of the valve core of the solenoid valve according to the finite element analysis method, and its balance mathematical model is established.…”

Section: Introductionmentioning

confidence: 99%

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Research on accurate modeling and control for pneumatic electric braking system of commercial vehicle based on multi-dynamic parameters measurement

Zhao

Yang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for those commercial vehicles that require higher regulation precision of braking force on four wheels when braking force distribution is carried out under some conditions. Due to the lagging information acquisition, most feedback-based control algorithms are difficult to further improve the dynamic response of PEBS. Meanwhile, feedforward-based control algorithms like predictive control perform well in improving dynamic performance but because of the large amount of computation and complexity of this kind of control algorithm, it cannot be applied in real-time on the single-chip microcomputer, and it is still in the stage of theoretical research at present. To address this issue and for the sake of engineering reliability, this article presents a logic threshold control scheme combining analogous model predictive control (AMPC) and proportional control. In addition, an experimental device for real-time measuring PEBS multi-dynamic parameters is built. After correcting the key parameters, the precise model is determined and the influence of switching solenoid valve on its dynamic response characteristics is studied. For the control scheme, numerical and physical validation is executed to demonstrate the feasibility of the strategy and for the performance of the controller design. The experimental results show that the dynamic model of PEBS can accurately reflect its pressure characteristics. Furthermore, under different air source pressures, the designed controller can stably control the pressure output of PEBS and ensure that the error is within 0.08 bar. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on accurate modeling and control for pneumatic electric braking system of commercial vehicle based on multi-dynamic parameters measurement

Zhao

Yang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

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show abstract

“…20 However, different from the servo-pneumatic system, the mechanical part of the PRV has an extremely short stroke and high speed, which results in a large stiffness of the system state transition equation due to the coupling between mechanical and fluid motion, therefore model-based control method can easily lead to severe oscillations. 13 In this circumstance, a motion decision algorithm that can combine the reliability of the model-based method and the flexibility of the reinforcement learning (RL) algorithm will be meaningful. 21 Combined with the characteristics of the solenoid valve, this paper proposes the idea of using an optimization method to solve the control problem caused by the response speed of the actuator.…”

Section: Introductionmentioning

confidence: 99%

“…What is more, the lack of direct measurement sensors for key states such as displacement and pressure in commercial products cannot be neglected. For the reasons mentioned above, robust controllers such as sliding mode control 11,12 and active disturbance rejection control (ADRC) are designed to be used in conjunction with observers such as luenberger observer 13 and extended state observer (ESO). 14–16 As far as the pneumatic system is concerned, Gulati and Barth 17 has explained in detail that the stability of the pressure observer can be derived directly from fluid properties, furthermore, an error corrective term based on force is designed to speed up convergence.…”

Section: Introductionmentioning

confidence: 99%

Pressure control based on reinforcement learning strategy of the pneumatic relays for an electric-pneumatic braking system

Shan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the electric-pneumatic braking system (EPBS), fast and accurate brake pressure regulation is critical to vehicle braking safety and is the basis for active safety functions. However, the lack of signal feedback, limited actuator response accuracy, and extremely strong model stiffness and nonlinearity pose problems for high-precision brake pressure regulation. To solve these problems, this article proposes a Q-learning-based control algorithm to regulate actuator instructions. First, the nonlinearity of the system and complicated actuator operating process is settled by a simplified mathematical model. Then, a decoupled pressure observing method is proposed to solve the observation problem caused by the coupling of mechanical and fluid motion. Finally, this paper proposes the idea of using an optimization method to solve the control problem caused by the response speed of the actuator, a Q-learning algorithm is used to settle the solenoid switching action to minimize response time and steady-state error. Both simulation and practical experiments are conducted to demonstrate the dependability of the model and effectiveness of the control method.

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“…The braking pressure output prosperities are susceptible to working conditions such as temperature, humidity and piping arrangement [11,12]. Moreover, after having undergone poor working conditions, mechanical wear can also affect the control of brake pressure [13]. These ubiquitous non-linearities place higher requirements on the pressure control of PEBS.…”

Section: Introductionmentioning

confidence: 99%

Pressure control for pneumatic electric braking system of commercial vehicle based on model predictive control

Zhao

Yang

2021

IET Intelligent Trans Sys

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Precise braking pressure control of a pneumatic electric braking system (PEBS) poses challenging non-linear control problems, since it can operate in several distinct discrete processes (the pressure increasing, holding and decreasing) by switching the on-off solenoid valves and the piston of relay valve moves irregularly. This article describes the development and experimental validation of a viable controller for an electronically controlled braking pressure based on the theory of model predictive control (MPC). The controller design consists of two parts. The non-linear characteristic is first studied to ensure the pressure response of PEBS when the opening time is 4-10 ms in a fixed period, followed by calculation and prediction of the optimal on-off time of inlet valve and release valve based on the current braking pressure to realize steady state. A logic rule is designed to ensure that the MPC controller and feedback control work coordinate to guarantee engineering reliability. A comprehensive system model is derived to help characterize system non-linearities and design the MPC predictive model. Simulations and experimental results are presented finally to show how the MPC and feedback control strategy can be successfully applied to solve the braking pressure control of a PEBS in a systematic way.

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Nonlinear controller design and testing for chatter suppression in an electric-pneumatic braking system with parametric variation

Cited by 19 publications

References 32 publications

Research on accurate modeling and control for pneumatic electric braking system of commercial vehicle based on multi-dynamic parameters measurement

Research on accurate modeling and control for pneumatic electric braking system of commercial vehicle based on multi-dynamic parameters measurement

Pressure control based on reinforcement learning strategy of the pneumatic relays for an electric-pneumatic braking system

Pressure control for pneumatic electric braking system of commercial vehicle based on model predictive control

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