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Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

Lookup NU author(s): Davut Izci, Dr John Hedley


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© The Author(s) 2021. This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

Publication metadata

Author(s): Izci D, Ekinci S, Zeynelgil HL, Hedley J

Publication type: Article

Publication status: Published

Journal: Transactions of the Institute of Measurement and Control

Year: 2022

Volume: 44

Issue: 2

Pages: 435-456

Print publication date: 01/01/2022

Online publication date: 19/08/2021

Acceptance date: 02/04/2021

ISSN (print): 0142-3312

ISSN (electronic): 1477-0369

Publisher: Sage


DOI: 10.1177/01423312211037967


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