For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
An Optimal Nonlinear Regulator Design with Neural Network and Fixed Point Theorem
Dawei CAI Yasunari SHIDAMA Masayoshi EGUCHI Hiroo YAMAURA Takashi MIYAZAKI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1993/05/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Neural Nets,Chaos and Numerics)
Category: Neural Nets--Theory and Applications--
neural network, nonlinearity, optimal control, regulator, fixed point problem,
Full Text: PDF>>
A new optimal nonlinear regulator design method is developed by applying a multi-layered neural network and a fixed point theorem for a nonlinear controlled system. Based on the calculus of variations and the fixed point theorem, an optimal control law containing a nonlinear mapping of the state can be derived. Because the neural network has not only a good learning ability but also an excellent nonlinear mapping ability, the neural network is used to represent the state nonlinear mapping after some learning operations and an optimal nonlinear regulator may be formed. Simulation demonstrates that the new nonlinear regulator is quite efficient and has a good robust performance as well.