Research on Trajectory Tracking of Unmanned Excavators Based on Nonlinear Model Predictive Control

Abstract The hydraulic excavator is a three-degree-of-freedom robotic arm consisting of the boom, arm, and bucket. To address the significant time delays and nonlinearities inherent in hydraulic excavators and achieve precise trajectory tracking, a Nonlinear Model Predictive Control(NMPC) algorithm is proposed. Specifically, the Hammerstein-Wiener structure is employed to model the nonlinear hydraulic system, with parameters identified from experimental data. Based on this model, an NMPC trajectory tracking algorithm is developed, which accounts for control signal and actuator constraints. To mitigate the inherent 0.5-second response delay of the hydraulic system, a predictive delay compensation strategy is introduced, where the predicted joint states for the next 0.5 seconds serve as real-time control references. Simulation results indicate that, compared to the Proportional-Integral-Derivative(PID) and fuzzy PID algorithms, the NMPC significantly improves tracking accuracy, with the maximum error of the bucket end controlled within 20 cm. Field experiments were carried out using an autonomous excavator based on Robot Operating System (ROS), and the results confirm that the maximum error of trajectory tracking is within 50 cm. These results validate the feasibility and robustness of the proposed nonlinear model predictive controller under real-world operating conditions.