RBF Network Adaptive Sliding Mode Control of Ball and Plate System Based on Reaching Law

Aiming at the slow approach speed and chattering problems of sliding mode variable structure control of the ball and plate system, a Radial Basis Function (RBF) network adaptive control scheme based on the reaching law is proposed. Firstly, a double sliding mode surface is defined, and the dual-speed self-adjusting approach parameters are designed on the basis of the power reaching law; secondly, the RBF neural network is used to approximate the unknown part of the nonlinear model to reduce the fuzzy gain. At the same time, the estimated value of the parameter is employed to replace the neural network weight adjustment, which makes the adaptive algorithm simpler. Finally, Lyapunov theory is used to prove the stability of the controller, and the effectiveness of the controller is verified by simulation.