DocumentCode :
1515815
Title :
Neural dynamic optimization for control systems. I. Background
Author :
Seong, Chang-Yun ; Widrow, Bernard
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., CA, USA
Volume :
31
Issue :
4
fYear :
2001
fDate :
8/1/2001 12:00:00 AM
Firstpage :
482
Lastpage :
489
Abstract :
The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. The main feature of NDO is that it enables neural networks to approximate the optimal feedback solution whose existence dynamic programming (DP) justifies, thereby reducing the complexities of computation and storage problems of the classical methods such as DP. This paper mainly describes the background and motivations for the development of NDO, while the two other subsequent papers of this topic present the theory of NDO and demonstrate the method with several applications including control of autonomous vehicles and of a robot arm, respectively
Keywords :
MIMO systems; computational complexity; dynamic programming; feedback; neural nets; optimal control; optimisation; autonomous vehicles; complexities; control systems; dynamic programming; neural dynamic optimization; nonlinear multi-input-multi-output systems; optimal feedback control; optimal feedback solution; robot arm; Computer networks; Control systems; Dynamic programming; Feedback control; MIMO; Neural networks; Neurofeedback; Optimal control; Optimization methods; Vehicle dynamics;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/3477.938254
Filename :
938254
Link To Document :
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