Title :
Adaptive Backstepping Control and Application for Strict-Feedback Nonlinear Systems with Mismatched Uncertainties
Author :
Xu, Zibin ; Min, Jianqing ; Ruan, Jian
Author_Institution :
MOE Key Lab. of Mech. Manuf. & Autom., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
Aiming at a class of strict-feedback nonlinear systems with mismatched uncertainties, an adaptive backstepping neural controller design is presented. By applying backstepping design strategy and online approaching uncertainties with fully tuned radial basis function (RBF) neural networks, the adaptive tuning rules are derived from the Lyapunov stability theory. To deal with the problem of extremely expanded operation quantity of backstepping method, a nonlinear tracking differentiator is introduced. The developed control scheme guarantees that all the signals of the closed-loop system are uniform ultimate boundedness. Experiment of an electro-hydraulic shaker shows the effectiveness of the proposed method.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control system synthesis; electrohydraulic control equipment; feedback; neurocontrollers; nonlinear control systems; radial basis function networks; uncertain systems; Lyapunov stability theory; adaptive backstepping neural controller design; adaptive tuning rules; closed-loop system; electrohydraulic shaker; mismatched uncertainties; nonlinear tracking differentiator; radial basis function neural networks; strict-feedback nonlinear systems; Adaptive control; Adaptive systems; Backstepping; Control systems; Lyapunov method; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Uncertainty; RBF neural networks; adaptive backstepping control; electro-hydraulic servo system; mismatched uncertainty nonlinear system;
Conference_Titel :
Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3490-9
DOI :
10.1109/PACIIA.2008.98
