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

2021 ◽  
Author(s):  
Yongtao Zhao ◽  
Yiyong Yang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Commercial Vehicle ◽  
Pressure Control ◽  
Braking System ◽  
Electric Braking

Research on Accurate Modeling and Control for Pneumatic Electric Braking System of Commercial Vehicle Based on Multi-Dynamic Parameters Measurement

2020 ◽  
Author(s):  
Yongtao Zhao ◽  
Yiyong Yang ◽  
Xiuheng Wu ◽  
Xingjun Tao
Keyword(s):  
Dynamic Response ◽  
Real Time ◽  
Predictive Control ◽  
Control Algorithm ◽  
Control Algorithms ◽  
Dynamic Parameters ◽  
Braking System ◽  
Control Scheme ◽  
Braking Force ◽  
Electric Braking

Abstract Accurate pressure control and fast dynamic response are vital to the pneumatic electric braking system (PEBS) for that commercial vehicles 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 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 are 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 8KPa. Compared with the traditional control algorithm, the rapidity is improved by 32.5%.

Adaptive Model Predictive Control Research on Regenerative Braking for Electric Bus Cruising Downhill

2016 ◽  
Vol 15 (03) ◽  
pp. 133-150 ◽  
Author(s):  
Zhao Guo-Zhu ◽  
Huang Xiang ◽  
Peng Xing
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Least Square ◽  
Mode Switching ◽  
Regenerative Braking ◽  
Adaptive Model ◽  
Recursive Least Square ◽  
Braking System ◽  
Adaptive Model Predictive Control

To use regenerative brake and mechanical brake co-operatively to maintain the constant speed and the braking energy can be regenerated as much as possible when vehicles travel downhill, the mathematical model of the braking system is established, and the adaptive model predictive control method is adopted to control the speed of vehicles. The recursive least square algorithm with the forgetting factor is used to identify the road gradient online. And then the control results of the adaptive model predictive control are compared with the results of PID control, simulation results show that the robustness and the stability of the adaptive model predictive control method are better. The speed can be maintained basic stability with the coordinated use of the regenerative braking and the mechanical braking. Meanwhile, the braking energy can be regenerated as much as possible as the regenerative braking system can be used as much as possible. Moreover, as the charge acceptance ability of the battery is restricted, the brake mode switching model is designed. The braking mode can be switched between the electro-mechanical braking system and mechanical braking system according to the SOC of the batteries.

scholarly journals Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 215
Author(s):  
Zhang ◽  
Lin
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Pressure Control ◽  
Backstepping Control ◽  
Experimental Results ◽  
Reduction Gear ◽  
Braking System ◽  
Adaptive Neural Network Control ◽  
Electric Braking ◽  
The Ideal

This paper proposes an adaptive backstepping control algorithm for electric braking systems with electromechanical actuators (EMAs). First, the ideal mathematical model of the EMA is established, and the nonlinear factors are analyzed, such as the deformation of the reduction gear. Subsequently, the actual mathematical model of the EMA is rebuilt by combining the ideal model and the nonlinear factors. To realize high performance braking pressure control, the backstepping control method is adopted to address the mismatched uncertainties in the electric braking system, and a radial basis function (RBF) neural network is established to estimate the nonlinear functions in the control system. The experimental results indicate that the proposed braking pressure control strategy can improve the servo performance of the electric braking system. In addition, the hardware-in-loop (HIL) experimental results show that the proposed EMA controller can satisfy the requirements of the aircraft antilock braking systems.

scholarly journals Gain-Scheduled Model Predictive Control for a Commercial Vehicle Air Brake System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 899
Author(s):  
Dawei Hu ◽  
Gangyan Li ◽  
Feng Deng
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Commercial Vehicle ◽  
Chamber Pressure ◽  
Nonlinear Mathematical Model ◽  
Proportional Valve ◽  
Linear Parameter Varying ◽  
Air Brake System ◽  
Gain Scheduled

This paper presents a control-oriented Linear Parameter-Varying (LPV) model for commercial vehicle air brake systems with the electro-pneumatic proportional valve based on the nonlinear mathematical model, a set of discrete-time linearized models at different target pressures with the q-Markov Cover system identification method. The scheduled parameters for the LPV model were the brake chamber pressure, which was controlled by the electro-pneumatic proportional valve. On the basis of the LPV model, a family of Model Predictive Control (MPC) controllers with a Kalman filter was designed at each operation point. Then, the gain-scheduled MPC was designed over the entire operating range with the switched strategy, which was validated by experimental data. Furthermore, compared with the PID controller, the performance of the system was improved with a gain-scheduled MPC controller.

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