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Erschienen in: International Journal of Machine Learning and Cybernetics 4/2011

01.12.2011 | Original Article

A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

verfasst von: Chih-Min Lin, Ming-Chia Li, Ang-Bung Ting, Ming-Hung Lin

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2011

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Abstract

This study presents a robust self-learning proportional-integral-derivative (RSPID) control system design for nonlinear systems. This RSPID control system comprises a self-learning PID (SPID) controller and a robust controller. The gradient descent method is utilized to derive the on-line tuning laws of SPID controller; and the \( \, H_{\infty } \, \) control technique is applied for the robust controller design so as to achieve robust tracking performance. Moreover, in order to achieve fast learning of PID controller, a particle swarm optimization (PSO) algorithm is adopted to search the optimal learning-rates of PID adaptive gains. Finally, two nonlinear systems, a two-link manipulator and a chaotic system are examined to illustrate the effectiveness of the proposed control algorithm. Simulation results show that the proposed control system can achieve favorable control performance for these nonlinear systems.

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Metadaten
Titel
A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm
verfasst von
Chih-Min Lin
Ming-Chia Li
Ang-Bung Ting
Ming-Hung Lin
Publikationsdatum
01.12.2011
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2011
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0021-4

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