Research on DBNFM-based anti-rollover control of active suspension and its influence on trajectory planning

Rollover accidents are considered as one of the most dangerous types of traffic collisions. To tackle the challenge of effectively managing objective switching and minimizing rollover risk, this study proposes a novel intelligent suspension control method that integrates a dynamic Bayesian network with T-S Fuzzy Membership (DBNFM). The Class-Attribute Contingency Coefficient (CACC) method is utilized to create a validated dataset for DBNFM rollover probability calculations. Based on varying rollover probabilities, the control objective can be specifically tailored towards enhancing ride comfort or anti-rollover stability through unique probability-related membership and robust control design approaches. Additionally, this innovative hybrid suspension control system is integrated into the vehicle trajectory planning process to enable more aggressive and urgent path adjustments. Simulation-based validation comprehensively demonstrates the method’s capabilities in enhancing roll stability and optimizing ride comfort. The car body roll angle of DBNFM is suppressed by 20% compared to passive suspension in double lane change condition. Meanwhile, DBNFM is capable of reducing body vertical acceleration to below 1 m/s 2 , resulting in a 50% decrease compared to optimal control and offering a highly efficient planning trajectory. These advancements establish a unified control paradigm that effectively balances the conflicting demands of comfort-oriented and stability-critical operational modes.