title: Adaptive control for mimo uncertain nonlinear systems using recurrent wavelet neural network.
abstract: Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort.
authors.affiliation: Department of Electrical Engineering, Yuan Ze University, No. 135, Far-Eastern Rd., Chung-Li, Tao-Yuan, 320, Taiwan. cml@saturn yzu.edu.tw.
authors.forename: Chih-Min
authors.initials: CM
authors.lastname: Lin
authors.forename: Ang-Bung
authors.initials: AB
authors.lastname: Ting
authors.forename: Chun-Fei
authors.initials: CF
authors.lastname: Hsu
authors.forename: Chao-Ming
authors.initials: CM
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D058067:Wavelet Analysis