Title :
RCMAC-Based Adaptive Control for Uncertain Nonlinear Systems
Author :
Peng, Ya-Fu ; Lin, Chih-Min
Author_Institution :
Dept. of Electr. Eng., Ching-Yun Univ., Chung-li
fDate :
6/1/2007 12:00:00 AM
Abstract :
An adaptive control system, using a recurrent cerebellar model articulation controller (RCMAC) and based on a sliding mode technique, is developed for uncertain nonlinear systems. The proposed dynamic structure of RCMAC has superior capability to the conventional static cerebellar model articulation controller in an efficient learning mechanism and dynamic response. Temporal relations are embedded in RCMAC by adding feedback connections in the association memory space so that the RCMAC provides a dynamical structure. The proposed control system consists of an adaptive RCMAC and a compensated controller. The adaptive RCMAC is used to mimic an ideal sliding mode controller, and the compensated controller is designed to compensate for the approximation error between the ideal sliding mode controller and the adaptive RCMAC. The online adaptive laws of the control system are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. In addition, in order to relax the requirement of the approximation error bound, an estimation law is derived to estimate the error bound. Finally, the simulation and experimental studies demonstrate the effectiveness of the proposed control scheme for the nonlinear systems with unknown dynamic functions
Keywords :
Lyapunov methods; adaptive control; cerebellar model arithmetic computers; control system synthesis; feedback; learning (artificial intelligence); neurocontrollers; nonlinear control systems; recurrent neural nets; uncertain systems; variable structure systems; Lyapunov stability theorem; adaptive control; approximation error bound; association memory space; feedback connection; recurrent cerebellar model articulation controller; sliding mode controller; uncertain nonlinear system; Adaptive control; Approximation error; Control systems; Feedback; Learning systems; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Sliding mode control; Adaptive control; nonlinear systems; recurrent cerebellar model articulation controller (RCMAC); sliding mode control (SMC); Algorithms; Artificial Intelligence; Biomimetics; Cerebellum; Computer Simulation; Feedback; Models, Statistical; Models, Theoretical; Nonlinear Dynamics; Pattern Recognition, Automated; Robotics;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2006.888761