The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini mencadangkan kawalan mod gelongsor menggunakan teknik LMI dan rangkaian neural kabur berulang (RFNN) berulang untuk kelas sistem tunda masa tak linear yang tidak pasti. Pertama, rangkaian neural kabur berulang TS baru (TS-RFNN) dibangunkan untuk memberikan pampasan yang lebih fleksibel dan berkuasa bagi ketidakpastian sistem. Kemudian, kawalan model gelongsor berasaskan TS-RFNN dicadangkan untuk sistem kelewatan masa yang tidak pasti. Secara terperinci, reka bentuk permukaan gelongsor diperoleh untuk mengatasi bentuk dinamik bukan Isidori-Bynes kanonik, masa tunda yang tidak diketahui dan ketidakpastian yang tidak sepadan. Berdasarkan kaedah Lyapunov-Krasoviskii, keadaan kestabilan asimptotik bagi gerakan gelongsor dirumuskan untuk menyelesaikan masalah Ketaksamaan Matriks Linear (LMI) yang tidak bergantung pada kelewatan yang berubah-ubah masa. Tambahan pula, ketidakpastian gandingan input juga diambil kira. Sistem terkawal keseluruhan mencapai kestabilan asimptotik walaupun mempertimbangkan pemodelan yang lemah. Sumbangan termasuk: i) permukaan gelongsor asimptotik direka bentuk daripada menyelesaikan LMI bebas kelewatan yang mudah dan boleh dibaca; dan ii) TS-RFNN lebih boleh direalisasikan (disebabkan oleh peraturan kabur yang lebih sedikit digunakan). Akhir sekali, keputusan simulasi menunjukkan kesahihan skim kawalan yang dicadangkan.
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Salinan
Tung-Sheng CHIANG, Chian-Song CHIU, "Sliding Mode Control of a Class of Uncertain Nonlinear Time-Delay Systems Using LMI and TS Recurrent Fuzzy Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 1, pp. 252-262, January 2009, doi: 10.1587/transfun.E92.A.252.
Abstract: This paper proposes the sliding mode control using LMI techniques and adaptive recurrent fuzzy neural network (RFNN) for a class of uncertain nonlinear time-delay systems. First, a novel TS recurrent fuzzy neural network (TS-RFNN) is developed to provide more flexible and powerful compensation of system uncertainty. Then, the TS-RFNN based sliding model control is proposed for uncertain time-delay systems. In detail, sliding surface design is derived to cope with the non-Isidori-Bynes canonical form of dynamics, unknown delay time, and mismatched uncertainties. Based on the Lyapunov-Krasoviskii method, the asymptotic stability condition of the sliding motion is formulated into solving a Linear Matrix Inequality (LMI) problem which is independent on the time-varying delay. Furthermore, the input coupling uncertainty is also taken into our consideration. The overall controlled system achieves asymptotic stability even if considering poor modeling. The contributions include: i) asymptotic sliding surface is designed from solving a simple and legible delay-independent LMI; and ii) the TS-RFNN is more realizable (due to fewer fuzzy rules being used). Finally, simulation results demonstrate the validity of the proposed control scheme.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.252/_p
Salinan
@ARTICLE{e92-a_1_252,
author={Tung-Sheng CHIANG, Chian-Song CHIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sliding Mode Control of a Class of Uncertain Nonlinear Time-Delay Systems Using LMI and TS Recurrent Fuzzy Neural Network},
year={2009},
volume={E92-A},
number={1},
pages={252-262},
abstract={This paper proposes the sliding mode control using LMI techniques and adaptive recurrent fuzzy neural network (RFNN) for a class of uncertain nonlinear time-delay systems. First, a novel TS recurrent fuzzy neural network (TS-RFNN) is developed to provide more flexible and powerful compensation of system uncertainty. Then, the TS-RFNN based sliding model control is proposed for uncertain time-delay systems. In detail, sliding surface design is derived to cope with the non-Isidori-Bynes canonical form of dynamics, unknown delay time, and mismatched uncertainties. Based on the Lyapunov-Krasoviskii method, the asymptotic stability condition of the sliding motion is formulated into solving a Linear Matrix Inequality (LMI) problem which is independent on the time-varying delay. Furthermore, the input coupling uncertainty is also taken into our consideration. The overall controlled system achieves asymptotic stability even if considering poor modeling. The contributions include: i) asymptotic sliding surface is designed from solving a simple and legible delay-independent LMI; and ii) the TS-RFNN is more realizable (due to fewer fuzzy rules being used). Finally, simulation results demonstrate the validity of the proposed control scheme.},
keywords={},
doi={10.1587/transfun.E92.A.252},
ISSN={1745-1337},
month={January},}
Salinan
TY - JOUR
TI - Sliding Mode Control of a Class of Uncertain Nonlinear Time-Delay Systems Using LMI and TS Recurrent Fuzzy Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 252
EP - 262
AU - Tung-Sheng CHIANG
AU - Chian-Song CHIU
PY - 2009
DO - 10.1587/transfun.E92.A.252
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2009
AB - This paper proposes the sliding mode control using LMI techniques and adaptive recurrent fuzzy neural network (RFNN) for a class of uncertain nonlinear time-delay systems. First, a novel TS recurrent fuzzy neural network (TS-RFNN) is developed to provide more flexible and powerful compensation of system uncertainty. Then, the TS-RFNN based sliding model control is proposed for uncertain time-delay systems. In detail, sliding surface design is derived to cope with the non-Isidori-Bynes canonical form of dynamics, unknown delay time, and mismatched uncertainties. Based on the Lyapunov-Krasoviskii method, the asymptotic stability condition of the sliding motion is formulated into solving a Linear Matrix Inequality (LMI) problem which is independent on the time-varying delay. Furthermore, the input coupling uncertainty is also taken into our consideration. The overall controlled system achieves asymptotic stability even if considering poor modeling. The contributions include: i) asymptotic sliding surface is designed from solving a simple and legible delay-independent LMI; and ii) the TS-RFNN is more realizable (due to fewer fuzzy rules being used). Finally, simulation results demonstrate the validity of the proposed control scheme.
ER -