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.

在电动气动制动系统 (EPBS) 中，快速准确的制动压力调节对车辆制动安全至关重要，是主动安全功能的基础。然而，缺乏信号反馈、有限的执行器响应精度以及极强的模型刚度和非线性给高精度制动压力调节带来了问题。为了解决这些问题，本文提出了一种基于 Q-learning 的控制算法来调节执行器指令。首先，通过简化的数学模型解决了系统的非线性和复杂的执行器操作过程。针对机械和流体运动耦合引起的观测问题，提出了一种解耦压力观测方法。最后，本文提出了使用优化方法解决执行器响应速度引起的控制问题的思路，采用 Q-learning 算法解决螺线管开关动作，以最小化响应时间和稳态误差。通过仿真和实际实验证明了模型的可靠性和控制方法的有效性。