Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks

Chien-Hsing SU; Cheng-Sea HUANG; Kuang-Yow LIAN

doi:10.1093/ietfec/e89-a.5.1446

Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks

Chien-Hsing SU, Cheng-Sea HUANG, Kuang-Yow LIAN

Full Text Views

0

Cite this

Summary :

A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.5 pp.1446-1453

Publication Date: 2006/05/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e89-a.5.1446

Type of Manuscript: PAPER

Category: Systems and Control

Cite this

Copy

Chien-Hsing SU, Cheng-Sea HUANG, Kuang-Yow LIAN, "Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E89-A, no. 5, pp. 1446-1453, May 2006, doi: 10.1093/ietfec/e89-a.5.1446.
Abstract: A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.5.1446/_p

Copy

@ARTICLE{e89-a_5_1446,
author={Chien-Hsing SU, Cheng-Sea HUANG, Kuang-Yow LIAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks},
year={2006},
volume={E89-A},
number={5},
pages={1446-1453},
abstract={A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.},
keywords={},
doi={10.1093/ietfec/e89-a.5.1446},
ISSN={1745-1337},
month={May},}

Copy

TY - JOUR
TI - Control Performance of Discrete-Time Fuzzy Systems Improved by Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1446
EP - 1453
AU - Chien-Hsing SU
AU - Cheng-Sea HUANG
AU - Kuang-Yow LIAN
PY - 2006
DO - 10.1093/ietfec/e89-a.5.1446
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2006
AB - A new control scheme is proposed to improve the system performance for discrete-time fuzzy systems by tuning control grade functions using neural networks. According to a systematic method of constructing the exact Takagi-Sugeno (T-S) fuzzy model, the system uncertainty is considered to affect the membership functions. Then, the grade functions, resulting from the membership functions of the control rules, are tuned by a back-propagation network. On the other hand, the feedback gains of the control rules are determined by solving a set of LMIs which satisfy sufficient conditions of the closed-loop stability. As a result, both stability guarantee and better performance are concluded. The scheme applied to a truck-trailer system is verified by satisfactory simulation results.
ER -