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

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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.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 61273260), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20121333120010), China Postdoctoral Science Foundation (No. 2013M530888,2014T70229), Natural Science Foundation of Hebei Province (No. F2014203208).

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  1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Xiao Li, Ying Wang, Ning Li, Minyu Han, Yinggan Tang & Fucai Liu

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  1. Xiao Li
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Correspondence to Yinggan Tang.

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Li, X., Wang, Y., Li, N. et al. Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization. Int. J. Mach. Learn. & Cyber. 8, 1595–1605 (2017). https://doi.org/10.1007/s13042-016-0530-2

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