Dual-Adaptive Preview Model Predictive Control for Path Tracking of Differential-Drive Tracked Vehicles

This study addresses the challenges of coordinating tracking accuracy and steering control stability in agricultural differential-drive tracked vehicles, particularly their adaptability to varying working conditions. An adaptive preview Model Predictive Control (MPC) path-tracking algorithm is developed using an electronically controlled modified differential-drive tracked vehicle as the test platform. A preview MPC controller is constructed based on kinematic analysis and a vehicle preview tracking error model, with a fuzzy controller optimizing the objective function’s weight matrix. A speed- and path-dependent preview-distance selection criterion is formulated, and an improved Sparrow Search Algorithm (SSA) provides an optional numerical implementation of preview-distance selection. The corresponding fixed-state objective admits a closed-form analytical solution. A tracked vehicle model was built based on a Robot Operating System (ROS), and a path tracking controller was built based on Simulink. Simulations and field tests at 0.5, 1.0, and 1.5 m/s evaluate lateral tracking error and steering angular-velocity statistics against separate fixed-preview baselines. Relative to the 3.0 m baseline, mean absolute lateral error decreases by 30.13–56.00% in simulation and 46.05–72.57% in field tests. Relative to the 1.0 m baseline, lateral-error changes depend on speed, while angular-velocity standard deviation decreases by 22.23–40.67% in simulation and 24.10–46.39% in field tests. These results characterize the tested controller configurations and show a condition-dependent trade-off between lateral error and steering-response variability.