Model-based MPC with adaptive weights for tracked vehicle trajectory tracking

Trajectory tracking is crucial for intelligent tracked vehicles, with model predictive control (MPC) being a widely used method due to its ability to handle predictions and constraints. However, conventional MPC based on kinematic models neglects the coupled longitudinal–lateral dynamics, leading to limited accuracy and stability. To address this, we propose an MPC strategy that integrates both kinematic and dynamic models for dual-motor-driven tracked vehicles. This approach uses lateral deviation, heading deviation, longitudinal velocity, and yaw rate as state variables, with motor torques as control inputs, explicitly capturing dynamic coupling and electric drive characteristics. Additionally, we introduce a deep Q-network (DQN)-based adaptive weight adjustment scheme to improve disturbance rejection and overcome the limitations of fixed MPC weights. This adaptive mechanism optimizes the weight matrix online under varying operating conditions. The proposed method is validated through MATLAB/Simulink–RecurDyn co-simulation and low-speed vehicle tests, demonstrating significant improvements in trajectory tracking, with reductions in lateral and heading deviations by 58.4% and 18.6%, respectively. Further, the DQN-based adaptive weighting leads to additional improvements, reducing lateral and heading deviations by 36.6% and 19.7%.