DocumentCode :
391284
Title :
Neural network based approximate output regulation in discrete-time uncertain nonlinear systems
Author :
Lan, Weiyao ; Huang, Jie
Author_Institution :
Dept. of Autom. & Comput.-Aided Eng., Chinese Univ. of Hong Kong, China
Volume :
2
fYear :
2002
fDate :
10-13 Dec. 2002
Firstpage :
1764
Abstract :
The existing approaches to the discrete-time nonlinear output regulation problem rely on the off-line solution of a set of mixed nonlinear functional equations known as discrete regulator equations or complex nonlinear systems, it is difficult to solve the discrete regulator equations even approximately. Moreover, for systems with uncertainty, these approaches cannot offer a reliable solution. By combining the approximation capability of the feedforward neural networks with an online parameter optimization mechanism, we develop an approach to solving the discrete-time nonlinear output regulation problem without solving the discrete regulator equations. The advantages of our approach is that it is much more efficient than the existing approaches, and it can handle systems with uncertain parameters.
Keywords :
discrete time systems; feedback; feedforward neural nets; function approximation; neurocontrollers; nonlinear control systems; uncertain systems; approximation capability; discrete-time uncertain nonlinear systems; feedforward neural networks; neural network based approximate output regulation; online parameter optimization mechanism; Approximation methods; Computational efficiency; Differential algebraic equations; Feedforward neural networks; Intelligent networks; Neural networks; Nonlinear equations; Nonlinear systems; Partial differential equations; Regulators;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
ISSN :
0191-2216
Print_ISBN :
0-7803-7516-5
Type :
conf
DOI :
10.1109/CDC.2002.1184778
Filename :
1184778
Link To Document :
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