An Optimal Nonlinear Regulator Design with Neural Network and Fixed Point Theorem
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E76-A No.5 pp.772-776
- Publication Date
- 1993/05/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section LETTER (Special Section on Neural Nets,Chaos and Numerics)
- Category
- Neural Nets--Theory and Applications--
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Dawei CAI, Yasunari SHIDAMA, Masayoshi EGUCHI, Hiroo YAMAURA, Takashi MIYAZAKI, "An Optimal Nonlinear Regulator Design with Neural Network and Fixed Point Theorem" in IEICE TRANSACTIONS on Fundamentals,
vol. E76-A, no. 5, pp. 772-776, May 1993, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e76-a_5_772/_p
Copy
@ARTICLE{e76-a_5_772,
author={Dawei CAI, Yasunari SHIDAMA, Masayoshi EGUCHI, Hiroo YAMAURA, Takashi MIYAZAKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Optimal Nonlinear Regulator Design with Neural Network and Fixed Point Theorem},
year={1993},
volume={E76-A},
number={5},
pages={772-776},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={May},}
Copy
TY - JOUR
TI - An Optimal Nonlinear Regulator Design with Neural Network and Fixed Point Theorem
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 772
EP - 776
AU - Dawei CAI
AU - Yasunari SHIDAMA
AU - Masayoshi EGUCHI
AU - Hiroo YAMAURA
AU - Takashi MIYAZAKI
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E76-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1993
AB - 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.
ER -
English
