Model predictive control (MPC) with prediction and control horizons under multivariable constraints can prompt field tracked vehicles to follow the reference path accurately. However, a kinematic model or a classic dynamic model of a vehicle is needed in MPC, and both of them must be linearized and hence the computation cost is large. Also, the parameters of a classic dynamic model are difficult to be measured. In this paper, system identification approach for estimated the linear state-space dynamic model of a field tracked vehicle in farm has been utilized. The dynamic model has been identified with more than 50% estimated fitting. Using the dynamic model, a linear MPC can be adopted, and hence the computation can be saved more than 2/3, compared with the conventional nonlinear MPC with a kinematic model. Furthermore, the tracked vehicle adopted the linear MPC with the dynamic model can achieve superior S-curve and L-shape path following.
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