Performance improvement of DC motor control system using PID controller with Kookaburra and Red Panda optimization algorithm

Abstract

In recent years, DC motors have found widespread use in numerous industrial applications. Precise speed control is required and would be achieved via the implementation of a Proportional–Integral–Derivative (PID) controller in the system. The tuning of controller gain has received significant attention, and conventional metaheuristic algorithms have been utilized for tuning. The conventional approaches produce an oscillatory response, which is minimized in the system by correct tuning, but it is a critical task in complex systems, and the metaheuristics algorithm is used to identify the better gain, particularly the swarm intelligence-based algorithm. The Kookaburra and Red Panda optimization algorithms were used to find the controller’s gain. Both algorithms’ approaches have been applied to the DC motor speed control system. The better solution of the algorithm is evaluated in the exploration and exploitation phases of the initial population in the metaheuristic algorithms. The quality of the solution is performed in the Integral Time Absolute Error (ITAE) objective function in Kookaburra Optimization Algorithm (KOA) and Red Panda Optimization Algorithm (RPOA). The time response and frequency response analyses were carried out in the system. The quick rise time and larger bandwidth obtained in KOA and the settling time in RPOA. In the KOA-based system, the improvement in rise time is 9.2–12.8% and bandwidth is 15 to 16.4% as compared with RPOA, which is also debated with other metaheuristic algorithms. To check the reliability of the operation, the robustness was analyzed in different cases in the range of ± 20% to ± 50%, and an improvement in the responses was claimed. The convergence of solutions in KOA and RPOA is at the 70th iteration and less ITAE was obtained in RPOA since the initial population with the same time complexity of algorithms.

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