Efficient Speed Control for DC Motors Using Novel Gazelle Simplex Optimizer

This paper addresses the design of an optimally executed proportional-integral-derivative (PID) controller, tailored for the speed regulation of a direct current (DC) motor. To achieve this objective, we present a novel hybrid algorithm, combining the gazelle optimization algorithm (GOA) with the effective simplex search method known as the Nelder-Mead (NM) technique. The fusion of these algorithms yields an innovative hybridized version, striking the balance between exploration and exploitation. The proposed approach, named the gazelle simplex optimizer (GSO), showcases great promise when applied to the task of controlling the speed regulation of a DC motor using the PID controller. To identify the optimal values for PID gains, we harness the power of a novel objective function as well, which guides the GSO in determining the most favorable controller settings. Rigorous comparative simulations are then undertaken, where we pit the GSO against several other algorithms, namely the reptile search algorithm, prairie dog optimization algorithm, weighted mean of vectors optimization, and the original GOA algorithm. These simulations allow us to assess the system’s behavior through various lenses, such as statistical tests, time and frequency domain responses, robustness analysis, and changes in the objective function. The evaluations from these comprehensive tests demonstrate the superiority of the GSO-based PID controlled DC motor speed regulation system. The GSO exhibits better performance than the alternative algorithms, providing solid evidence of its effectiveness. Furthermore, the proposed GSO approach outperforms other reported PID tuning methods, affirming its prowess in achieving superior speed regulation for DC motors.