Adaptive sliding mode control of flexible beam using RBF neural controller

An adaptive sliding mode controller using radial basis function (RBF) network is proposed to approximate the unknown system dynamics for cantilever beam. Neural network controller is designed to approximate the unknown system model. In the presence of unknown model uncertainties and external disturbances, sliding mode controller is employed to compensate for such system nonlinearities and improve the tracking performance. On-line neural network (NN) weight tuning algorithms are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. Numerical simulation for cantilever beam is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory vibration suppression performance.