Optimal FOPID controller design for DV motor speed control using ant lion optimization algorithm

Waseem, Saba, Irfan, Muhammad, Amin, Arslan Ahmed, Rahman, Saifur, Alwadie, Hatim and Al Dawsari, Saleh 2026. Optimal FOPID controller design for DV motor speed control using ant lion optimization algorithm. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 42 (5) , 118. 10.23967/j.rimni.2026.10.74825

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Abstract

DC motors are frequently utilized in industrial and automation applications where accurate speed control is crucial. Although conventional Proportional-Integral-and Derivative (PID) controllers are widely utilized, their constant gain values make them less effective in managing dynamic loads and disturbances. It’s difficult to get optimal transient and steady-state performance with traditional PID tuning methods. To overcome these limitations, more adaptable and dependable control systems are needed. This study introduces a novel control strategy by optimizing a Fractional-Order PID (FOPID) controller using the Ant Lion Optimization (ALO) method. Mathematical modeling is used to determine the DC motor’s transfer function. An ALO, a metaheuristic algorithm, is then implemented to improve five FOPID parameters using Integral Timeweighted Absolute Error (ITAE). The simulation is done in MATLAB-Simulink software. According to the findings, the enhanced ALO-FOPID controller decreased settling time (0.0728 s) and rising time (0.0455 s) when compared to the PID controller, which is taken as a reference. It is noted that the proposed ALO-tuned FOPID demonstrated enhanced response over the conventional methods, demonstrating the usefulness of bioinspired algorithms for precision control applications. The comparison of the proposed methodology is also done with other studies during different operating conditions. The results show that intelligent optimizationbased control in industrial systems is feasible, which aids in the creation of reliable and flexible automation solutions.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0213-1315
Date of First Compliant Deposit:	10 June 2026
Date of Acceptance:	12 March 2026
Last Modified:	24 Jul 2026 10:56
URI:	https://orca.cardiff.ac.uk/id/eprint/187509

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