  In this paper, we present a systematic approach for high-performance and
efficient trajectory tracking control of autonomous wheel loaders. With the
nonlinear dynamic model of a wheel loader, nonlinear model predictive control
(MPC) is used in offline trajectory planning to obtain a high-performance
state-control trajectory while satisfying the state and control constraints. In
tracking control, the nonlinear model is embedded into a Linear Parameter
Varying (LPV) model and the LPV-MPC strategy is used to achieve fast online
computation and good tracking performance. To demonstrate the effectiveness and
the advantages of the LPV-MPC, we test and compare three model predictive
control strategies in the high-fidelity simulation environment. With the
planned trajectory, three tracking control strategies LPV-MPC, nonlinear MPC,
and LTI-MPC are simulated and compared in the perspectives of computational
burden and tracking performance. The LPV-MPC can achieve better performance
than conventional LTI-MPC because more accurate nominal system dynamics are
captured in the LPV model. In addition, LPV-MPC achieves slightly worse
tracking performance but tremendously improved computational efficiency than
nonlinear MPC. A video with loading cycles completed by our autonomous wheel
loader in the simulation environment can be found here:
https://youtu.be/QbNfS_wZKKA.
