Cascaded MPC-ADRC trajectory tracking control for electric-drive unmanned tracked vehicles

Abstract Heavy-duty unmanned tracked vehicles are characterized by large inertia and complex operating conditions, which may lead to vehicle instability and trajectory divergence during trajectory tracking control. To address the trajectory tracking problem of heavy-duty unmanned vehicles, a series control method combining Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) is proposed. In this framework, the MPC algorithm based on a small-angle assumption calculates the desired yaw rate by solving local path points. The first-order linear ADRC controller then drives the vehicle to track the desired yaw rate by controlling the motor torques on both sides. Compared with conventional control methods, the proposed control method achieves decoupled lateral-longitudinal dynamics for tracked vehicles while incorporating yaw rate response dynamics into the control scheme. Simulation results demonstrate that this control method enables accurate trajectory tracking for heavy-duty tracked vehicles.