Optimizing full vehicle active suspension model with advanced reinforcement learning controller

Optimizing car suspension systems remains a key challenge in the search for safer and more comfortable driving. This paper explores an innovative approach using artificial intelligence and reinforcement learning to design and evaluate an active suspension model. Mathematical modeling of vehicle dynamics is integrated into a reward-based control strategy to optimize suspension performance. An in-depth evaluation methodology is implemented to analyze system responses in various road and driving conditions. The results show a significant reduction, reaching more than 90%, of the disturbances felt by the passengers, with variations in RMS maintained within a comfort range. This major breakthrough opens up opportunities for intelligent and adaptable suspension systems, propelling the car industry towards a smoother, safer and more comfortable driving.