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Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator

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Abstract

This paper proposes the neural network (NN) predictive controller that combines the advantages of NN and predictive control for the automatic voltage regulator (AVR). The NN predictive controller is suggested as a new intelligence controller rather than the conventional controllers for the AVR. This is the first application of the NN predictive controller for AVR. There are five parameters of the NN predictive controller which need a proper tuning to get a good performance by using the NN predictive controller. In recent papers, the parameters of NN predictive controller are typically set by trial and error or by the designer’s expertise. The imperialist competitive algorithm (ICA) is introduced in this paper as a new artificial intelligence technique instead of the trial-and-error or the designer’s expertise methods to get the optimal parameters of NN predictive controller in order to overcome the deviations of the voltage. The performance of the designed NN predictive controller based on the ICA is compared with the designed NN predictive controller based on the genetic algorithm and the conventional proportional–integral–derivative controller based on Ziegler–Nichols technique. The comparison emphasizes the superiority of the suggested NN predictive controller based on the ICA.

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Abbreviations

N 1 :

The minimal prediction horizon of the output

N 2 :

The maximal prediction horizon of the output

N u :

The control horizon

u′:

Tentative control signal

y r :

The target response

y m :

The network model response

ρ :

The weight of the control signal

β :

A number > 1

d :

The distance between colony and imperialist

γ :

A limit angle

V ref :

The reference voltage

V t :

The output terminal voltage

e :

The error signal

u :

The control signal

K A :

The amplifier gain

T A :

The amplifier time constant

K E :

The exciter gain

T E :

The exciter time constant

K G :

The generator gain

T G :

The generator time constant

K S :

The sensor gain

T S :

The sensor time constant

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Authors and Affiliations

  1. Electrical Power and Machines Department, Faculty of Engineering (Shoubra), Benha University, 108 Shoubra St., B. O. Box 11241, Cairo, Egypt

    M. Elsisi

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Appendix

Appendix

The typical values of AVR system are given below [2]:

$$K_{\text{A}} = 10;\;T_{\text{A}} = 0.1\,{\text{s}};\;K_{\text{E}} = 1;\;T_{\text{E}} = 0.4 \,{\text{s}};\;K_{\text{G}} = 1;\;T_{\text{G}} = 1.0\,{\text{s}};\;K_{\text{s}} = 1;\;T_{\text{s}} = 0.05\,{\text{s}};$$

where boundary values of the plant variables are given below [3]:

$$10 \, \le \, K_{\text{A}} \le \, 40;\; \, 0.02 \, \le \, T_{\text{A}} \le \, 0.1; \, \;1 \, \le \, K_{\text{E}} \le \, 10;\; \, 0.4 \le T_{\text{E}} \le 1.0; \, \;0.7 \le K_{\text{G}} \le 1;\; \, 1.0 \le T_{\text{g}} \le 2.0;\; \, 0.9 \le K_{\text{s}} \le 1.1;\; \, 0.001 \le T_{\text{s}} \le 0.06.$$

Imperialist algorithm parameters: number of countries = 100; number of initial imperialists = 2; number of iteration = 100.

Genetic algorithm parameters: The genetic algorithm in the MATLAB toolbox is used with its default parameters.

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Elsisi, M. Design of neural network predictive controller based on imperialist competitive algorithm for automatic voltage regulator. Neural Comput & Applic 31, 5017–5027 (2019). https://doi.org/10.1007/s00521-018-03995-9

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