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
fDate :
8/1/2001 12:00:00 AM
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;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/3477.938254