基于 MPC-MFAC 的双侧独立电驱动无人履带车辆轨迹跟踪控制

The model mismatch caused by the simplified model and uncertainty of external environment are the main reasons for the trajectory tracking error. Especially for the unmanned tracked vehicle, its complex physical characteristics and working environment magnify the adverse effects of these two factors. To solve this problem, this paper combines the model-based and data-based control methods, and proposes a trajectory tracking control method for the dual independent electric drive unmanned tracked vehicle based on a model predictive control algorithm (MPC) combined with a model-free adaptive control algorithm (MFAC) as compensation. Firstly, based on balancing modeling accuracy and solution time, the MPC is used for feedforward solution. Then, for the inevitable differences between the simplified model in the MPC and the actual vehicle model and environmental uncertainty, the MFAC algorithm is constructed based on the dynamic tracking effect for compensation. That is, the error between the actual trajectory of the vehicle and the trajectory predicted by the model is used to correct the speed control quantities of the dual tracks solved by the MPC in real time. The simulation results show that this method can suppress the influence of internal and external uncertainties of the system to a certain extent, and improve the trajectory tracking control accuracy of the dual independent electric drive unmanned tracked vehicle in a dynamic environment.