Simulation and Control System Design of a Suspension Systems Test Rig Machine Using Particle Swarm Optimization (PSO) Methods

This study presents an application of particle swarm optimization (PSO) for adjusting PID parameters for a magnetorheological (MR) damper test rig. An electro-hydraulic mechanism is developed for damper dynamic testing that requires high-force applications. By implementing the PID control scheme with feedback from sensors, this system aims to achieve the targeted displacements despite various frequencies and loads. The manual adjustment of the parameters can be challenging and time consuming. Therefore, the PID parameters were optimized using three PSO methods, namely Classic PSO, Adaptive Comprehensive Learning PSO (ACL PSO), and Constriction PSO. Each method was applied to tune the PID controller for the control system, aiming to minimize the error between reference and predicted outputs while considering performance metrics such as overshoot and oscillations. In general, the results for either step and sine responses indicate that while Classic PSO is the fastest, ACL PSO and Constriction PSO achieve higher accuracy due to their enhanced exploration and stability mechanisms. The PID parameters were obtained with Mean Absolute Percentage Error (MAPE) values of 1.935% for tested conditions. The findings of this research show that PID control effectively achieves the desired movement.