August 17, 2026

Labeled diagram showing the working cycle of a wheel loader and dump truck. The dump truck is positioned horizontally at the top with an unloading point marked. A pile of material is shown on the right. The wheel loader moves between four labeled points: ① Loading Point near the pile, ② Turning Point, ③ Unloading Point at the dump truck, and ④ returning path to the pile. Arrows trace the loader’s movement path through these numbered steps.

Autonomous Wheel Loader Trajectory Tracking Control Algorithm Using LPV-MPC

Technology Category

Abstract

Autonomy in construction and mining vehicles is a valuable prospect, allowing machinery to function without direct human intervention and functioning 24/7. This technology streamlines this process, creating an algorithm for effective trajectory-tracking in large, hinged vehicles.

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What It Is

An algorithm designed to compute and drive the trajectory of large, hinged vehicles for use in autonomous construction and mining equipment.

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Value Proposition

This algorithm is more accurate for system dynamics in hinged vehicles, achieves better tracking performance, and improves computational efficiency, saving energy in autonomous computing systems.

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Applicable Markets

Construction, construction vehicles, autonomous vehicles, mining, mining vehicles.

Benefit

Most construction equipment is composed of a front body and a rear body, which are attached by a joint or hinge. The two can articulate with respect to each other, which introduces nnonlinear dynamics. Accurately modeling nonlinear dynamics in real-time consumes a great deal of processing power, so most current methods either assume a linear system or do not consider the angle of articulation. 

Current solutions are either computationally expensive or too inaccurate for large-scale use. The present technology resolves this issue, providing an algorithm that accurately models nonlinearity in hinged-vehicle trajectory. Though offline computing and various controllers, this algorithm can effectively model and control the movement of large construction and mining machinery in a variety of situations.

Improved computational efficiency translates to decreased energy consumption and initial computer costs. Improved accuracy results in a scalable system that can be implemented industry wide.

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Researchers

Tianyi He

Ruitao Song

USU Department: Mechanical and Aerospace Engineering

Developed in cooperation with:
Baidu USA.

USU Reference No.  C22013

Publications
  • R. Song, Z. Ye, L. Wang, T. He and L. Zhang, "Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC," 2022 American Control Conference (ACC), Atlanta, GA, USA, 2022, pp. 2063-2069, doi: 10.23919/ACC53348.2022.9867662. https://ieeexplore.ieee.org/document/9867662
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