DocumentCode :
840501
Title :
RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology
Author :
Chih-Min Lin ; Li-Yang Chen ; Chiu-Hsiung Chen
Author_Institution :
Dept. of Electr. Eng., Yuan Ze Univ., Jhongli City
Volume :
18
Issue :
3
fYear :
2007
fDate :
5/1/2007 12:00:00 AM
Firstpage :
708
Lastpage :
720
Abstract :
A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat´s lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system
Keywords :
Lyapunov methods; MIMO systems; adaptive control; asymptotic stability; cerebellar model arithmetic computers; neurocontrollers; nonlinear control systems; observers; recurrent neural nets; uncertain systems; variable structure systems; Barbalat lemma; Lyapunov stability theorem; MIMO uncertain nonlinear systems; RCMAC hybrid control; Taylor linearization technique; adaptive laws; approximation error bound; asymptotic stability; biped robot; multiple-input-multiple-output uncertain nonlinear systems; recurrent cerebellar model articulation controller; sliding-mode technology; system uncertainty; uncertainty observer; Approximation error; Control system synthesis; Control systems; Estimation error; Linearization techniques; MIMO; Nonlinear control systems; Nonlinear systems; Sliding mode control; Uncertainty; Biped robot; multiple-input–multiple-output (MIMO); nonlinear systems; recurrent cerebellar model articulation controller (RCMAC); sliding-mode control (SMC); Algorithms; Artificial Intelligence; Computer Simulation; Feedback; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer); Nonlinear Dynamics; Pattern Recognition, Automated; Robotics; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2007.891198
Filename :
4182394
Link To Document :
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