Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method

Abstract The electromagnet levitation control system is the core component of maglev trains, which has a significant influence on the performance of the maglev train. However, the control system suffers from the essential strong nonlinear and open-loop unstable. Moreover, the model uncertainty and many exogenous disturbances make the controller design even harder. In this paper, the mathematical model of maglev system is established firstly. Then, using the nonlinear transformation method, the affine nonlinear mathematical model of the maglev system is obtained without any linear approximation. Based on the presented model, we design a sliding mode controller based on the exponential reaching law preliminarily and the stability is proved. Since the control characteristics of the maglev system are highly uncertain and time varying with external disturbance, a radial basis function (RBF) neural network estimator is added to the proposed controller. To improve the convergence speed and better satisfy the requirements of real-time control, the minimum parameter learning method is adopted to replace the weights in the neural network without model information. The boundedness and convergence of the presented control law are proved by Lyapunov method. Finally, both simulation and experiment results are included to verify the effectiveness of the proposed control strategy.