ADAPTIVE CONTROL FOR MIMO UNCERTAIN NONLINEAR SYSTEMS USING RECURRENT WAVELET NEURAL NETWORK

摘要

Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, goodngeneralization capability and information storing ability. With these advantages, this paper proposes annRWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinearnsystems. The RBAC system is composed of a neural controller and a bounding compensator. Thenneural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator cannprovide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it isnshown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, thenproposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-springdampernmechanical system and a two-link robotic manipulator system. Simulation results verify that thenproposed RBAC system can achieve favorable tracking performance with desired robustness without anynchattering phenomenon in the control effort