Adaptive Tracking Control for a Class of Uncertain Non-affine Delayed Systems Subjected to Input Constraints Using Self Recurrent Wavelet Neural Network

In this work an adaptive tracking control strategy for a class of non affine delayed systems subjected to actuator saturation is proposed. Self recurrent wavelet neural network (SRWNN) is used to approximate the uncertainties present in the system as well as to identify and compensate the nonlinearities introduced in the system due to actuator saturation. By using suitable transformation the system under consideration is first converted into an affine like form and subsequently an adaptive backstepping control strategy is developed to assure the stable tracking of nonlinear non affine system. In addition robust control terms are also designed to attenuate the approximation error due to SRWNN. Adaptation laws are developed for the online tuning of the wavelet parameters and the stability of the overall system is assured by using the Lyapunov- Krasovskii functional. The effectiveness of theoretical development is verified by a numerical example.