Reinforcement Learning-Based Optimal Tracking Control for Levitation System of Maglev Vehicle With Input Time Delay

The running environment of a magnetic levitation (maglev) vehicle is complex, with problems such as track irregularity, external disturbance, varying system parameters, and time delays, which bring great challenges to the design of a high-performance levitation controller. In this article, an adaptive neural network controller with input delay compensation and a control parameter optimization scheme is proposed for the electromagnetic levitation system of a maglev vehicle, which can solve the key engineering problems of external disturbance, input time delay, and time-varying mass. Aiming at the problem of input time delay, a sliding-mode surface with time-delay compensation is constructed, and a double-layer neural network and adaptive law are utilized to approximate the uncertain dynamics; thus, a finite time adaptive tracking control law is proposed. Based on the Lyapunov method, the stability of the proposed controller in finite time is analyzed. The proposed method does not only update the input and output weights of the neural network online, but also introduces reinforcement learning (Actor-Critic network) to optimize the key controller parameter in real time and enhance system robustness. Simulation and experimental results show that the proposed controller can effectively suppress the air gap vibration with time delay and uncertain dynamics, and significantly improve the performance of levitation control.

磁悬浮车辆的运行环境复杂，存在轨道不平顺、外部干扰、系统参数变化和时滞等问题，这给高性能悬浮控制器的设计带来了巨大挑战。本文针对磁悬浮车辆的电磁悬浮系统，提出了一种具有输入时滞补偿的自适应神经网络控制器以及一种控制参数优化方案，能够解决外部干扰、输入时滞和质量时变等关键工程问题。针对输入时滞问题，构建了一个具有时滞补偿的滑模面，并利用双层神经网络和自适应律来逼近不确定动力学，从而提出了一种有限时间自适应跟踪控制律。基于李雅普诺夫方法，分析了所提控制器在有限时间内的稳定性。所提方法不仅在线更新神经网络的输入和输出权重，还引入强化学习（演员 - 评论家网络）实时优化关键控制器参数并增强系统的鲁棒性。仿真和实验结果表明，所提控制器能够有效抑制具有时滞和不确定动力学的气隙振动，并显著提高悬浮控制性能。