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

Abstract

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.