Hybrid PID Parameter Adjustment for an Active Vehicle Suspension System Using Python-Based Analysis

Active suspension can deliver better ride comfort and stability than a passive layout system, but only when the controller is properly tuned. This work proposes a simple tuning method: retaining Proportional-Integral-Derivative (PID) structure and employing two off-the-shelf optimizers (Optuna and Particle Swarm) to select the gains. The full-vehicle 7-Degrees of freedom (DOF) benchmark of Kumar et al. was used as a virtual test bench to compare four controllers: passive, Kumar et al. (2020) PID. A manually tuned baseline, and the two optimizer-tuned versions. The cost function combines RMS body acceleration, pitch and roll angular rates, peak actuator force and jerk across three simulation scenarios (bump, speed-breaker, cornering), with soft penalties for actuator saturation, suspension travel, and tire lift-off. The Optuna gains cut the global cost by 19% relative to the manual baseline and by 8.5% relative the Kumar et al. (2020) PID, primarily by reducing the peak actuator force from 2.7 kN to 1.78 kN. A 100-vehicle Monte-Carlo study (±20% on sprung mass, ±15% on stiffness and damping) confirms that the performance advantage is robust to variations in the nominal parameters.