A Comparative Study of Bio Inspired Optimization Algorithms for Enhanced PID Control of BLDC Motor Speed

This paper presents a systematic comparative study of bio-inspired optimization algorithms for tuning Proportional–Integral–Derivative (PID) controllers in brushless DC (BLDC) motor speed regulation. Four representative metaheuristics are considered, namely the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Butterfly Optimization Algorithm (BOA). Unlike many previous works that focus on a single algorithm or on limited pairwise comparisons, this study develops a unified optimization and simulation framework in which all methods are applied to the same BLDC motor model, under identical operating conditions and performance criteria. The PID gains are obtained by minimizing the Integral of Time-weighted Absolute Error (ITAE), while controller performance is independently assessed using the Integral of Absolute Error (IAE), Root Mean Squared Error (RMSE), and key transient response metrics to rigorously quantify tracking accuracy, dynamic behavior, and disturbance rejection. Numerical results clearly show that all bio-inspired approaches outperform conventional fixed-gain PID control. More importantly, the comparative analysis highlights the specific strengths of each algorithm: PSO provides the most balanced and robust performance over all indices, GWO achieves very fast response with low overshoot, and BOA yields an almost overshoot-free response at the expense of slower convergence. These findings not only confirm the effectiveness of bio-inspired tuning, but also offer practical guidelines for selecting appropriate optimization strategies in high-performance BLDC motor drive applications.