Performance Optimization of PID Controllers for DC Machine Drives Using PSO, ACO, and Hybrid PSO-ACO Algorithms

PID controllers are utilised extensively in the domain of electric motors and drives. The values of the PID controller have a direct impact on the controller's characteristics. Establishing optimal values is imperative to enhance the efficacy of control mechanisms. Consequently, a multitude of optimization algorithms have been developed. Employing these algorithms facilitates the optimisation of the controller's optimal values with greater efficiency, requiring less experience and a shorter timeframe. In this study, the parameters of the PID controller employed in the motor drive developed for a direct current (DC) motor are optimised by three distinct heuristic optimisation methods: The following optimization methods are used: Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and PSO-ACO, which is a combination of these two methods. The execution of simulations is conducted within the MATLAB environment, with a subsequent comparative analysis of control performances. This study proposes a pioneering optimisation approach that integrates the PSO and ACO algorithms. The PID controller attains the reference value in the most efficient timeframe through this methodology. The simulation results show that the PSO-ACO method demonstrates optimal performance, followed by PSO and ACO.