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

Abstract

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.

Uncertainty-Estimation-Based Prescribed Performance Pressure Control for Train Electropneumatic Brake Systems

“…The coupling between the pressure and the air temperature complicates the system dynamics. Moreover, in the electropneumatic brake system, the discrete nature of on/off solenoid valves and the high nonlinearity caused by the air compressibility, airflow, and disturbances also increase the difficulty of accurate pressure control [3].…”

A Modeling and Control Algorithm for a Commercial Vehicle Electronic Brake System Based on Vertical Load Estimation

“…Hamada et al [3] conducted a comprehensive discussion on the basic principles of regenerative braking systems. In order to solve the problem of limited actuator response accuracy in EBSs, Shan et al [4] proposed a control algorithm based on Q-learning to adjust the actuator commands. The traditional commercial vehicle electronic braking system has problems such as slow pressure response, large dynamic error, and unsatisfactory braking effect during the braking process.…”