Double lane change trajectory tracking control of unmanned vehicle based on active MPC algorithm

To meet the requirements for accuracy and dynamic response in unmanned vehicle trajectory tracking, this paper proposes a solution based on the Model Predictive Control (MPC) algorithm. First, an unmanned vehicle dynamic model is constructed to mathematically describe the vehicle’s motion laws. On this basis, an MPC trajectory tracking controller is designed: by discretizing the system state equation, building multi-objective optimization functions and constraints, the trajectory tracking problem is converted into a quadratic programming problem, and high-precision tracking of the reference trajectory is finally achieved. To verify the algorithm’s performance, a double lane change trajectory simulation experiment is established, focusing on analyzing the trajectory tracking effect, state error convergence characteristics, control quantity variation rules, and computational efficiency. The results show that the MPC controller enables rapid convergence of initial errors; in double lane change trajectory tracking, both position and heading errors maintain centimeter-level accuracy, and the control variable curve is smooth, fully satisfying physical constraints. This scheme provides efficient and reliable technical support for unmanned vehicle trajectory tracking.