Metaheuristic Approaches to Tune PID Controller for Ball on Plate System

This paper presents a comprehensive study on the optimization of PID controller parameters for a ball & plate system through the utilization of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The objective of the study is to attain precise steady-state response while shortening settling time and minimizing overshoot. The assessment of controller performance is conducted using the Integral Absolute Error (IAE) cost function. The study highlights the limitations of conventional tuning techniques and the need for metaheuristic optimization algorithms, particularly when system models and variables are incomplete or imperfect, and demonstrates that both GA and PSO can improve the performance of the controller, but GA outperforms PSO in terms of fitness function value and optimal gains. The study also highlights the importance of accurate system models and variables in achieving optimal performance. The results of this study hold significant implications for the application of PID controllers and optimization algorithms based on metaheuristic principles in various applications, particularly in process industries. The study demonstrates the potential of metaheuristic algorithms for optimizing PID controllers and highlights the need for further research in this area.