Optimization of the Backstepping Control Parameters of an Active Electrohydraulic Suspension to Improve Passenger Comfort and Road Handling

This study introduces an innovative optimization strategy for Electro-Hydraulic Active Suspension Systems (EHASS), combining game theory with Particle Swarm Optimization (PSO) to tune backstepping control parameters. Unlike conventional approaches relying on manual tuning or trial-and-error, our method systematically optimizes these parameters, ensuring a well-balanced trade-off between ride comfort and road handling. The optimization process considers worst-case road disturbances, leading to a 79.5% reduction in tracking error, a 44.7% decrease in VDV, and a 51.2% improvement in Crest Factor, complying with ISO 2631 standards. Comprehensive validation across ten road profiles, including highly irregular terrains, confirms the robustness of the proposed method. Additionally, a comparison with Genetic Algorithm (GA)-based optimization highlights that PSO achieves superior convergence and performance. These findings establish a new benchmark for intelligent suspension control, making our approach a strong candidate for real-world automotive applications.