# Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC

arXiv: https://arxiv.org/abs/2203.08944 · HTML: https://mlsanity.com/paper/2203.08944/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [Learning the Approach During the Short-loading Cycle Using Reinforcement Learning](https://mlsanity.com/paper/2406.13366/) (2024)
- [Autonomous Wheel Loader Navigation Using Goal-Conditioned Actor-Critic MPC](https://mlsanity.com/paper/2409.15717/) (2024)

## Abstract (arXiv)

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.