[1]LI Jun-tao,JIA Ying-min.Robust H∞ control for a class of uncertain nonlin ear systems based on adaptive neural networks[J].CAAI Transactions on Intelligent Systems,2007,2(6):54-59.
Copy

Robust H∞ control for a class of uncertain nonlin ear systems based on adaptive neural networks

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 6 Page number: 54-59 Column: 学术论文—机器学习 Public date: 2007-12-25
Title:
Robust H∞ control for a class of uncertain nonlin ear systems based on adaptive neural networks
Author(s):
LI Jun-tao; JIA Ying-min
The Seventh Research Division, Beihang University, Beijing 100 083, China
Keywords:
H∞ control;  adaptive neural network;  nonlinear systems;  robotic systems
CLC:
TP273
DOI:
-
Abstract:
This paper presents a novel robust H∞ control scheme for a class of uncer tain nonlinear multiinput timevarying systems. A simplified HamiltonJacobi li ke inequality was proposed by introducing two adaptive neural network approximat ors. Then, a nonlinear H∞ controller and a compensation controller for match ed uncertainties were designed, thus eliminating the boundedness assumpti ons of the input perturbation term and the optimal approximation errors in the estimator. Finally, a simulation of the robost tracking control for robotic syst em was run that verified t he research results.
References:
［1］WANG W Y，CHAN M L，HSU C C J, et al. H∞ Tracking ba sed sliding mode control for uncertain nonlinear systems via an adaptive fuzzyneural appr oach［J］. IEEE Transactions on Systems, Man, and Cyber netics,2002，32(3)：483- 492.
［2］CHEN B S，LEE C H,CHANG Y C. Tracking design of uncertain nonl in ear SISO systems: adaptive fuzzy approach ［J］. IEEE Trans Fuzzy Syst, 1996(4) ：32-43.
［3］LIN T C，WANG C H,LIU H L.Observerbased indirect adaptive fu z zyneural tracking control for nonlinear SISO systems using VSS and approach ［ J］. Fuzzy Sets Syst,2004，143：211-232.
［4］ISIDORI A，ASTOLFI A. Disturbance attenuation and control via measur ement feedback in nonlinear systems ［J］. IEEE Trans Autom Contr, 1992，37(11)：1283-1293.
［5］CHANG Y C.Intelligent robust control for uncertain nonli near timevaryi ng systems and its application to robotic systems ［J］. IEEE Transactions on Systems, Man, and Cybernetics,2005， 35(6)：1108-1119.
［6］SHEN T L,TAMURA K. Robust control of uncertain nonlinear system via state feedback ［J］. IEEE Trans Robot Autom,2004，40(4)：766-768.
［7］LEU Y G,WANG W Y,LEE T T. Robust adaptive fuzzyneural control lers for uncertain nonlinear systems ［J］. IEEE Trans Robot Autom,1999，15：805 -817.
［8］PARK J H,SEO S J,PARK G T. Robust adaptive fuzzy controller fo r nonlinear systems using estimation of bounds for approximation errors ［J］. F uzzy Sets Syst,2003，133(1)：19-36.
［9］LIN W. Global robust stabilization of minimumphase nonlinear systems wi th uncertainty ［J］. Automatica,1997，33(3)：453-462.
［10］KHALIL H K. Adaptive output feedback control of nonlinear systems re pre sented by inputoutput models ［J］. IEEE Trans Autom Contr, 1996，41(2)：177-1 78.
Similar References:

Memo

-

Last Update: 2009-05-09

Copyright © CAAI Transactions on Intelligent Systems