Coordinated control of yaw and roll stability in heavy vehicles considering dynamic safety requirements

Abstract

In the field of heavy vehicle stability research, traditional safety requirements are often based on static scenario settings. However, the complexity and variability of actual road environments require safety control strategies that can be adapted to different driving conditions and environmental changes in real-time. To address this challenge, the paper proposes a coordinated control strategy for yaw and roll stability that considers the dynamic safety requirements. First, a quantitative analysis method for vehicle stability is proposed based on the dissipated energy theory, taking into account the lateral-vertical dynamics coupling characteristics. Additionally, a dynamic safety requirements identification model is developed by integrating the vehicle's future driving risk prediction algorithm. To meet dynamic safety requirements, a dynamic weight model predictive control method based on randomized ensembled double Q-learning reinforcement learning is designed. This method adjusts the control weights of yaw and roll stability online to flexibly address various destabilization risks, aiming to achieve more precise and efficient stability control. Through simulation and experimental verification, the results demonstrate that the proposed coordinated control strategy can effectively enhance the stability and safety of heavy vehicles in complex and dynamic driving environments.