DocumentCode
3275156
Title
Neuro-based optimal regulator for a class of system with uncertainties
Author
Xu, Bing Bong ; Tsuji, Toshio ; Hatagi, Michio ; Kaneko, Makoto
Author_Institution
Fac. of Eng., Hiroshima Univ., Japan
fYear
1996
fDate
2-6 Dec 1996
Firstpage
692
Lastpage
696
Abstract
This paper proposes a neuro-based optimal regulator (NBOR) for a class of system with uncertainties. In this paper, we show how the neural network output compensates the control input based on the Riccati equation and how the compensatory solution of the Riccati equation is estimated by the least-squares method. Then, the NBOR is applied to systems with uncertainties in order to illustrate its effectiveness and applicability
Keywords
Riccati equations; compensation; least squares approximations; neurocontrollers; optimal control; uncertain systems; NBOR; Riccati equation; compensatory solution; least-squares estimation; neuro-based optimal regulator; uncertainties; Control system synthesis; Control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Optimal control; Regulators; Riccati equations; Robust control; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
0-7803-3104-4
Type
conf
DOI
10.1109/ICIT.1996.601683
Filename
601683
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