Performances improvement of DC Motor using a Fractional Order Adaptive PID Controller optimized by Genetic Algorithm

In the past 20 years, scientists and engineers have rediscovered fractional calculus and have begun using it in more and more domains, most notably control theory. This study introduces a fractional adaptive PID (FAPID) controller which incorporates an additional parameter to enhance the performance of a conventional adaptive PID (APID) controller. A comparative analysis is conducted between the APID and FAPID controllers optimized using the metaheuristic Genetic Algorithm (GA). The evaluation uses a linearized model of the DC motor control system. The results demonstrate that FAPID controllers significantly outperform conventional APID controllers, particularly regarding rise time, settling time, overshoot, and mean absolute error. Among the proposed designs, the integration of FAPID proves to be the most effective in achieving a balance between responsiveness and stability, exhibiting exceptional robustness and adaptability to variations in DC motor and environmental conditions. This method can be extended to various fractional and integer systems to enhance their efficiency and reduce noise disturbance.