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An effective control design approach based on novel enhanced aquila optimizer for automatic voltage regulator

An effective control design approach based on novel enhanced aquila optimizer for automatic... This paper presents a new metaheuristic algorithm by enhancing one of the recently proposed optimizers named Aquila optimizer (AO). The enhanced AO (enAO) algorithm is constructed by employing a novel modified opposition-based learning (OBL) mechanism and Nelder-Mead (NM) simplex search method. The novel modified OBL aids the AO in further diversification while the NM method increases the intensification. The enAO algorithm is first demonstrated to have more extraordinary ability than the original AO algorithm by employing challenging benchmark functions from the CEC 2019 test suite. The constructed enAO algorithm is proposed to design a PID plus second-order derivative (PIDD2) controller used in an automatic voltage regulator (AVR) system. To reach better efficiency, a novel objective function is also proposed in this paper. Initially, the proposed enAO-PIDD2 approach is demonstrated to be superior in terms of transient and frequency responses along with robustness and disturbance rejection compared to other available and best performing PID, fractional order PID (FOPID), PID acceleration (PIDA), and PIDD2 controllers tuned with different practical algorithms. Moreover, the superior performance of the proposed approach is also demonstrated comparatively using other available techniques for the AVR system reported in the last six years. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

An effective control design approach based on novel enhanced aquila optimizer for automatic voltage regulator

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References (84)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature B.V. 2022
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-022-10216-2
Publisher site
See Article on Publisher Site

Abstract

This paper presents a new metaheuristic algorithm by enhancing one of the recently proposed optimizers named Aquila optimizer (AO). The enhanced AO (enAO) algorithm is constructed by employing a novel modified opposition-based learning (OBL) mechanism and Nelder-Mead (NM) simplex search method. The novel modified OBL aids the AO in further diversification while the NM method increases the intensification. The enAO algorithm is first demonstrated to have more extraordinary ability than the original AO algorithm by employing challenging benchmark functions from the CEC 2019 test suite. The constructed enAO algorithm is proposed to design a PID plus second-order derivative (PIDD2) controller used in an automatic voltage regulator (AVR) system. To reach better efficiency, a novel objective function is also proposed in this paper. Initially, the proposed enAO-PIDD2 approach is demonstrated to be superior in terms of transient and frequency responses along with robustness and disturbance rejection compared to other available and best performing PID, fractional order PID (FOPID), PID acceleration (PIDA), and PIDD2 controllers tuned with different practical algorithms. Moreover, the superior performance of the proposed approach is also demonstrated comparatively using other available techniques for the AVR system reported in the last six years.

Journal

Artificial Intelligence Review – Springer Journals

Published: Feb 1, 2023

Keywords: Enhanced Aquila optimizer; Opposition-based learning; Nelder-Mead simplex search method; Automatic voltage regulator; Controller design

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