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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2017

21.04.2016 | Original Article

Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization

verfasst von: Xiao Li, Ying Wang, Ning Li, Minyu Han, Yinggan Tang, Fucai Liu

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2017

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Abstract

Automatic voltage regulator (AVR) system is an important equipment in power system for maintaining the terminal voltage of the generator at a specific level. Recently, fractional order PID controller has been designed for AVR system. However, many fractional order PID controller designing methods need to calculate various performance indices in time domain and frequency domain in the process of parameter tuning, which is a tedious and complex process and satisfactory performance can not be obtained. In this paper, a new fractional order PID controller designing method is proposed AVR system based on Bodes reference model. The optimal parameters of FOPID controller is obtained through minimizing the integrated absolute error (IAE) between the output of the Bodes ideal reference model and that of the plant. Particle swarm optimization (PSO) is responsible to search the solution of the IAE criterion, i.e., the parameters of FOPID controller. Extensive simulations and comparisons show that the designed FOPID controller has more excellent performance. Meanwhile, PSO algorithm is effective for searching the optimal FOPID controller parameters.
Vorheriger Artikel Cross kernel distance minimization for designing support vector machines
Nächster Artikel Anti-periodic solutions for cellular neural networks with oscillating coefficients in leakage terms

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Metadaten
Titel
Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization
verfasst von
Xiao Li
Ying Wang
Ning Li
Minyu Han
Yinggan Tang
Fucai Liu
Publikationsdatum
21.04.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0530-2

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