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Titolo:
GA-based practical compensator design for a motion control system
Autore:
Ito, K; Iwasaki, M; Matsui, N;
Indirizzi:
Nagoya Inst Technol, Dept Elect & Comp Engn, Nagoya, Aichi 4668555, Japan Nagoya Inst Technol Nagoya Aichi Japan 4668555 goya, Aichi 4668555, Japan
Titolo Testata:
IEEE-ASME TRANSACTIONS ON MECHATRONICS
fascicolo: 2, volume: 6, anno: 2001,
pagine: 143 - 148
SICI:
1083-4435(200106)6:2<143:GPCDFA>2.0.ZU;2-1
Fonte:
ISI
Lingua:
ENG
Soggetto:
ROBUST;
Keywords:
2-degrees-of-freedom control; autonomous compensator design; genetic algorithms; optimization; robust control; vibration suppression;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
8
Recensione:
Indirizzi per estratti:
Indirizzo: Ito, K Nagoya Inst Technol, Dept Elect & Comp Engn, Nagoya, Aichi 4668555,Japan Nagoya Inst Technol Nagoya Aichi Japan 4668555 ichi 4668555, Japan
Citazione:
K. Ito et al., "GA-based practical compensator design for a motion control system", IEEE-A T M, 6(2), 2001, pp. 143-148

Abstract

This paper presents an evolutionary algorithm for the robust motion controller design in mechatronics systems using a genetic algorithm (GA). The motion control system is composed of a robust 2-degrees-of-freedom compensatorbased on the coprime factorization description. Conventional controller design approaches to the optimization for compensator-free parameters essentially require complicated numerical procedures under the given control specifications, In this research, a simple and practical algorithm for the compensator design for motion control systems has been proposed. Using the optimization ability of the GA, the proposed algorithm is able to autonomously tune the optimal combination of the compensator-free parameters to satisfy the specified motion control performance. The effectiveness of the proposed optimal design can be verified by experiments using a prototype, paying attention to the system robustness against variations of mechanical parametersand the fast convergence of the optimization by the GA.

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Documento generato il 05/12/20 alle ore 20:06:37