Path tracking Control Method Based on Self-adaptive Variable Model Predictive control

As a fundamental technology in the field of intelligent vehicles, path tracking control has been extensively implemented to improve the performance, safety, and efficiency of automotive systems. The Model Predictive Control (MPC) approach is predominantly utilized for vehicle path tracking control. However, conventional MPC methods exhibit certain limitations. This paper addresses the challenges of inadequate real-time performance and suboptimal dynamic adaptability of the prediction horizon in MPC for distributed four-wheel independently driven electric vehicles (DFID-EVs) during path tracking. Initially, a three-degree-of-freedom (3DOF) vehicle dynamics model is developed for path tracking. Subsequently, a simplified nonlinear tire model based on the Tire Magic Formula is introduced to derive the longitudinal and lateral forces for the model. Furthermore, a data-driven adaptive law is designed to autonomously adjust the parameters of the variable-step MPC (VMPC) in response to varying operational conditions, thereby achieving high-precision tracking control and enhanced stability. Finally, comprehensive simulations and experimental validations are conducted to substantiate the theoretical and practical efficacy of the proposed method. The proposed self-adaptive VMPC (SVMPC) method demonstrates robust control performance and superior adaptability under complex driving scenarios.