DocumentCode :
292020
Title :
Applications of optimal control theory using artificial neural networks
Author :
Martinez, J.M. ; Barret, C. ; Houkari, M. ; Meyne, P. ; Dominguez, M.
Author_Institution :
Centre d´´Etudes de Saclay, Gif-sur-Yvette, France
Volume :
2
fYear :
1994
fDate :
2-5 Oct 1994
Firstpage :
1464
Abstract :
This paper shows neural networks capabilities in optimal control applications of nonlinear dynamic systems. Our method is based on a classical method concerning the direct research of the optimal control using gradient techniques. We show that neural approach and backpropagation paradigm are able to solve efficiently equations relative to necessary conditions for an optimizing solution. We have taken into account the known capabilities of neural networks in approximation functions. And for dynamic systems, we have generalized the indirect learning of inverse model adaptive architecture that is capable of defining an optimal control in relation to a temporal criterion
Keywords :
adaptive control; backpropagation; neurocontrollers; nonlinear control systems; nonlinear dynamical systems; optimal control; approximation functions; artificial neural networks; backpropagation; inverse model adaptive architecture; nonlinear dynamic systems; optimal control; temporal criterion; Adaptive control; Artificial neural networks; Backpropagation; Control theory; Equations; Inverse problems; Neural networks; Nonlinear equations; Optimal control; Process control; Programmable control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2129-4
Type :
conf
DOI :
10.1109/ICSMC.1994.400052
Filename :
400052
Link To Document :
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