Comparison of mabsa, PSO and GWO of PI-PD controller for dc motor - Nur Naajihah, Ab Rahman (2024)
A DC motor is an essential component of industrial systems, such as conveyor systems. The controls provided by DC motors typically include functions such as position and speed regulation. This research focuses on speed control in the DC motor system. An effective approach to enhance the efficiency of a DC motor system is to incorporate a reliable controller that employs a suitable optimization technique. Proportional integralproportional derivative (PI-PD) controllers are widely employed in the industry due to their superior benefits compared to standard proportional integral derivative (PID) controllers. Swarm intelligence, classified as a subset of artificial intelligence, was employed to govern the PI-PD controllers' parameters. The aim of this study is to optimize the speed control capabilities of DC motor systems by implementing the PI-PD controller. Three distinct mathematical models pertaining to the DC motor system are derived from a thorough analysis of previous research. The swarm intelligence group selected Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Modified Adaptive Bats Sonar Algorithms (MABSA) to optimize the parameters of the PI-PD controller. In order to maximize the efficiency of the DC motor system using a PIPD controller, the Yahya and Yusoff performance criterion method is utilized to minimize any deviations from the desired performance. To simulate the DC motor system with a PI-PD controller that was optimized by several swarm intelligence algorithms, MATLAB/Simulink software was utilized. By comparing the capability of the DC motor system with a PI-PD controller optimized by the PSO, GWO algorithm, and MABSA, the transient response has been evaluated. An evaluation is underway regarding the steady state error, rise time, settling time, peak time, and maximum overshoot. In order to showcase and confirm the strength of swarm intelligence, the fitness function of the Yahya and Yusoff method is additionally bolstered by statistical analysis. MABSA exhibited outstanding performance in terms of accuracy, consistency, and disturbance rejection during the implementation of a PI-PD controller for optimizing the DC motor speed system. The system demonstrated its effectiveness by reducing the transient response aspects by 40% to 90% and the standard deviation factor by 70% to 99% when compared to the PSO and GWO algorithms. In general, when enhancing the DC motor system, the modified adaptive bats sonar algorithm (MABSA) is an exceptionally effective optimization technique that consistently achieves an optimal outcome.