LMI-Based Neurocontroller for State-Feedback Guaranteed Cost Control of Discrete-Time Uncertain System
Summary :
The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. It is newly shown that although the neurocontroller is included in the discrete-time uncertain system, the robust stability for the closed-loop system and the reduction of the cost are attained.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.8 pp.1903-1911
- Publication Date
- 2005/08/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.8.1903
- Type of Manuscript
- Special Section PAPER (Special Section on Recent Advances in Circuits and Systems--Part 2)
- Category
- Neural Networks and Fuzzy Systems
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Keyword
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Hiroaki MUKAIDANI, Yasuhisa ISHII, Nan BU, Yoshiyuki TANAKA, Toshio TSUJI, "LMI-Based Neurocontroller for State-Feedback Guaranteed Cost Control of Discrete-Time Uncertain System" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 8, pp. 1903-1911, August 2005, doi: 10.1093/ietisy/e88-d.8.1903.
Abstract: The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. It is newly shown that although the neurocontroller is included in the discrete-time uncertain system, the robust stability for the closed-loop system and the reduction of the cost are attained.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.8.1903/_p
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@ARTICLE{e88-d_8_1903,
author={Hiroaki MUKAIDANI, Yasuhisa ISHII, Nan BU, Yoshiyuki TANAKA, Toshio TSUJI, },
journal={IEICE TRANSACTIONS on Information},
title={LMI-Based Neurocontroller for State-Feedback Guaranteed Cost Control of Discrete-Time Uncertain System},
year={2005},
volume={E88-D},
number={8},
pages={1903-1911},
abstract={The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. It is newly shown that although the neurocontroller is included in the discrete-time uncertain system, the robust stability for the closed-loop system and the reduction of the cost are attained.},
keywords={},
doi={10.1093/ietisy/e88-d.8.1903},
ISSN={},
month={August},}
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TY - JOUR
TI - LMI-Based Neurocontroller for State-Feedback Guaranteed Cost Control of Discrete-Time Uncertain System
T2 - IEICE TRANSACTIONS on Information
SP - 1903
EP - 1911
AU - Hiroaki MUKAIDANI
AU - Yasuhisa ISHII
AU - Nan BU
AU - Yoshiyuki TANAKA
AU - Toshio TSUJI
PY - 2005
DO - 10.1093/ietisy/e88-d.8.1903
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2005
AB - The application of neural networks to the state-feedback guaranteed cost control problem of discrete-time system that has uncertainty in both state and input matrices is investigated. Based on the Linear Matrix Inequality (LMI) design, a class of a state feedback controller is newly established, and sufficient conditions for the existence of guaranteed cost controller are derived. The novel contribution is that the neurocontroller is substituted for the additive gain perturbations. It is newly shown that although the neurocontroller is included in the discrete-time uncertain system, the robust stability for the closed-loop system and the reduction of the cost are attained.
ER -
English
