Research on trajectory tracking control of tracked vehicles based on hydraulic motor system identification and Laguerre-MPC

To address the high computational cost of model predictive control and the high complexity associated with physics-based modeling of hydraulic drive systems in trajectory tracking of tracked unmanned vehicles, a hierarchical control framework is proposed to enhance trajectory tracking performance. In the upper-layer control, a Laguerre function-based model predictive control (Laguerre-MPC) strategy is developed to reduce the computational burden while maintaining control performance. In the lower-layer control, Hammerstein-Wiener identification is employed to establish control models for the left and right hydraulic motors, thereby avoiding the modeling complexity inherent to hydraulic systems. Moreover, a proportional-integral-derivative (PID) controller is incorporated into the lower-layer control to improve disturbance rejection during operation. Simulation results indicate that Laguerre-MPC yields substantially lower computational complexity than conventional MPC, with the average time required for a single optimization being only 24.3% of that required by conventional MPC, which improves the real-time capability of the control algorithm. Furthermore, field experiments are conducted on a tracked unmanned vehicle equipped with relevant sensors, including speed tracking and trajectory tracking tests under specified operating conditions. The results confirm the effectiveness of the proposed framework: compared with the conventional MPC + PID scheme, the proposed method achieves higher tracking accuracy, improving the average speed tracking accuracy by 28.7%, the straight-line trajectory tracking accuracy (root mean square error) by 65.5%, and the curved trajectory tracking accuracy (root mean square error) by 10.1%. The proposed framework provides a practical and efficient solution for trajectory tracking control of tracked unmanned vehicles with clear engineering applicability.