Research on trajectory tracking control of tracked vehicles based on hydraulic motor system identification and...

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.