High-Performance DC Motor Speed Control via Fractional Modeling and Algorithmic FOPID Tuning

This study introduces an innovative control strategy for a DC motor speed control system, referred to as a black box approach. The methodology involves system identification through two models: a fractional-order model, increasingly prevalent in identification processes, and a conventional integer-order model. The identification phase employs advanced heuristic optimization techniques, including particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), and genetic algorithms (GA). The subsequent control phase utilizes a fractional-order proportional-integral-derivative (FOPID) controller, widely recognized as a leading fractional-order control solution. The FOPID controller is characterized by five parameters: proportional gain ( Kₚ ), integral gain ( KI ), derivative gain ( Kd ), integral order ( λ ), and derivative order ( μ ). These parameters are optimized using the aforementioned algorithms to enhance the performance of the fractional-order model, particularly in terms of energy efficiency during the stabilization phase, compared to the traditional integer-order model. Performance is evaluated using the integral of time and absolute error (ITAE) criterion, alongside standard metrics such as overshoot and settling time. A robustness test, involving parameter variations, is conducted to validate the effectiveness of the proposed approach. Experimental validation is performed using Matlab/Simulink interfaced with an Arduino Uno board to determine the parameters of a real-world DC motor. The results demonstrate that fractional calculus significantly enhances the efficacy of DC motor speed control.