Control and optimization of a half-car active suspension system with classical and modified PID-based controllers using metaheuristic techniques

In automobiles and road vehicles such as buses, high-performance suspension systems are essential to ensure passenger comfort by minimizing the effects of road disturbances. Therefore, effective control of suspension systems is of great importance. This study investigates the control of a half-car active suspension system using Proportional-Integral-Derivative (PID), Fractional Order Proportional-Integral-Derivative (FOPID), Fractional Order Proportional-Integral + Fractional Order Derivative (FOPI + FOPD), Tilt-Integral-Derivative (TID), and Proportional-Integral-Derivative Acceleration (PIDA) controllers. The tuning of these controllers was performed using five different metaheuristic optimization algorithms: the Artemis Optimizer (AO), Escape Algorithm (ESC), Genghis Khan Shark Optimizer (GKSO), A Novel Weighted Mean of Vectors (INFO), and Moss Growth Optimization (MGO). The aim of this work is to determine the most efficient controller-algorithm combination for optimal system performance. Each controller was individually paired with each optimization algorithm, resulting in 25 distinct simulations. Performance was evaluated using the Integral Time-weighted Absolute Error (ITAE) as the objective function. Among all configurations, the ESC algorithm achieved the lowest ITAE value. Although the more complex controller structures were expected to outperform the classical PID, the PID controller achieved the best overall result, surpassing expectations. This outcome highlights that simple controller architectures, when properly tuned, can still deliver highly effective performance and should not be underestimated in control system design.