Toggle Main Menu Toggle Search

Open Access padlockePrints

Fractional Order PID Design based on Novel Improved Slime Mould Algorithm

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

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

© 2022 Taylor & Francis Group, LLC. This study attempts to maintain the terminal voltage level of an automatic voltage regulator (AVR) and control the speed of a direct current (DC) motor using a fractional order proportional integral derivative (FOPID) controller. The best parameters of the controller have been adjusted using a novel meta-heuristic algorithm called opposition-based hybrid slime mold with simulated annealing algorithm. The proposed algorithm aims to improve the original slime mold algorithm in terms of exploitation and exploration using simulated annealing and opposition-based learning, respectively. A time domain objective function was adopted as performance index to design the FOPID-based AVR and DC motor systems. The initial performance evaluation was carried out using unimodal and multimodal benchmark functions. The results confirmed the superior exploration and exploitation capabilities of the developed algorithm compared to the other state-of-the-art optimization algorithms. The performance of the proposed algorithm has also been assessed through statistical tests, time domain and frequency domain simulations along with robustness and disturbance rejection analyses for both DC motor and AVR systems. The proposed algorithm has shown superior capabilities for the respective systems compared to the other state-of-the-art optimization algorithms used for the same purpose.


Publication metadata

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

Publication type: Article

Publication status: Published

Journal: Electric Power Components and Systems

Year: 2022

Pages: Epub ahead of print

Online publication date: 07/04/2022

Acceptance date: 09/12/2021

ISSN (print): 1532-5008

ISSN (electronic): 1532-5016

Publisher: Taylor and Francis Ltd

URL: https://doi.org/10.1080/15325008.2022.2049650

DOI: 10.1080/15325008.2022.2049650


Altmetrics

Altmetrics provided by Altmetric


Share