AI-Powered Hybrid Controller to Improve Passenger Comfort Considering Changes in the Sprung Mass of the Vehicle

Smart suspensions have significantly improved passenger comfort and vehicle stability compared to their passive counterparts. This manuscript explores the integration of artificial intelligence (AI) into hybrid suspension control systems to enhance vehicle stability and ride comfort under conditions where suspended mass changes. A one-quarter-vehicle model is employed to simulate and evaluate the performance of a hybrid control strategy, which combines skyhook and groundhook methods using a dynamic weighting factor (α). This investigation considers an everyday situation where the sprung mass of a vehicle changes considerably when passengers enter or exit the automobile, impacting the suspension performance. Reinforcement learning techniques are utilized to optimize α, achieving an acceptable balance between passenger comfort and vehicle stability. Simulation results show significant improvements in the dynamic response of the sprung mass compared to traditional passive systems, while keeping vehicle stability. Although improvements in road holding are incremental, simulation effort validates the AI-based hybrid system’s potential for refinement and practical application. Validation in MATLAB-Simulink demonstrates the system’s adaptability to varying road conditions and load distributions. The findings highlight the transformative role of AI in suspension control, paving the way for real-time implementation, advanced algorithms, and integration into full-vehicle models. This study contributes to the ongoing development of intelligent suspension systems toward vehicle performance advancement by improving passenger comfort and road holding.