Chattering-Free Adaptive Wavelet Neural Network Controller Design for a Class of Nonlinear Systems

In order to ensure the conventional adaptive wavelet neural network control systems' stability, a compensator could be designed to dispel the approximation error. The most frequently used of compensator is like a sliding-mo de control which maybe cause chattering phenomena to wear the bearing mechanism. To tackle this problem, this paper proposes a chattering-free adaptive wavelet neural network control (CAWNNC) system. The proposed CAWNNC system is composed of a neural controller and a smooth compensator. The neural control uses a fuzzy wavelet neural network to online approximate an ideal controller and the smooth compensator is used to remove the chattering phenomena of conventional sliding-mode control completely. Then, the proposed CAWNNC approach is applied to two chaotic nonlinear systems to investigate its effectiveness. Through the simulatio n results, the proposed CAWNNC scheme can achieve favorable tracking performance and the convergence of the tracking error and control parameters can be accelerated by the developed PI adaptation learning algorithm.