Comparative optimization of PIDN and fractional order controllers for a quarter car active suspension system using recent metaheuristic algorithms

Active suspension systems are crucial for improving vehicle ride comfort and handling under road disturbances. However, tuning the parameters of advanced controllers in such systems remains a complex challenge due to the nonlinear nature of vehicle dynamics. This study investigates the control of a quarter car active suspension model using two advanced controller structures: the Proportional-Integral-Derivative with filter (PIDN) and the hybrid Fractional Order PI plus PD (FOPI + FOPD) controller. To enhance controller performance, six recent metaheuristic algorithms-Educational Competition Optimizer (ECO), Escape Optimization Algorithm (ESC), Fata Morgana Algorithm (FATA), Jason Healing Optimization (JHO), Memory, Evolutionary Operator and Local Search Based Improved Grey Wolf Optimizer (MELGWO), and Starfish Optimization Algorithm (SFOA)-are employed for parameter tuning. Simulation results, obtained in the MATLAB/Simulink environment, demonstrate that the ESC-optimized FOPI + FOPD controller achieves the most effective vibration suppression, significantly outperforming classical and fuzzy logic-based approaches in terms of peak overshoot, settling time, and control stability. These findings emphasize the importance of selecting suitable controller structures in conjunction with robust optimization techniques for intelligent suspension system design.