Model predictive control based on Q-learning for magnetic levitation platform system

Abstract

A model predictive control (MPC) method based on Q-learning algorithm, named QMPC, is proposed for weakly damped, nonlinear and open-loop unstable magnetic levitation platform (MLP) systems. In addition, the design of MPC controller for the MLP system, the state space of the MLP system airgap, the action space of the predictive horizon and control horizon, the reward and punishment function are also included in this research. Based on the Simscape and MATLAB/Simulink, the joint simulation of the MLP control system is realized. Compared with PID controller and traditional MPC controller, the simulation results show that QMPC controller has better disturbance rejection ability and tracking performance under six working conditions.