DocumentCode :
1676563
Title :
Robust control for linear system based on gradient flow neural network
Author :
Yu, Zhigang ; Shen, Yongliang ; Song, Shenmin ; Sun, Laijun
Author_Institution :
Key Lab. of Electron. Eng., Coll. of Heilongjiang Province, Harbin, China
fYear :
2010
Firstpage :
4337
Lastpage :
4341
Abstract :
A gradient flow algorithm model developed for the on-line robust pole assignment is proposed for solving Sylvester equations. The algorithm shows to be capable of synthesizing linear feedback control systems via on-line computing feedback gain matrix and desired closed-loop poles. Meanwhile, the close-loop system matrix is least sensitive to perturbation or uncertainty, and uniformly asymptotically stable in largely range. Simulation results are shown that the proposed approach is suitable to problem of robust stabilization for nonlinear system and on-line robust pole assignment.
Keywords :
asymptotic stability; closed loop systems; control system synthesis; feedback; gradient methods; linear systems; matrix algebra; neurocontrollers; nonlinear control systems; robust control; Sylvester equation; asymptotic stability; close loop system matrix; closed loop pole; feedback gain matrix; gradient flow algorithm; linear feedback control systems synthesis; linear system; neural network; nonlinear system; online robust pole assignment; robust control; Artificial neural networks; Equations; Linear systems; Mathematical model; Robust control; Robustness; State feedback; Gradient flow neural network; On-linear pole assignment; Robust control; Sylvester equation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
Type :
conf
DOI :
10.1109/WCICA.2010.5554022
Filename :
5554022
Link To Document :
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