Research on Coordinated Longitudinal–Vertical Control of Articulated Mining Trucks Using Extension Theory

This research addresses the coupling issue between speed tracking and vertical posture in articulated unmanned mining trucks within unstructured environments. An extension theory-based coordinated control strategy is proposed, incorporating both articulation joint safety and vehicle stability. The control framework employs extension theory to classify operational modes based on articulation angle and velocity deviation. For longitudinal motion, active disturbance rejection control (ADRC) is adopted to mitigate the influence of varying payload mass and road slope on speed tracking performance. For vertical dynamics, a soft actor–critic (SAC) algorithm regulates active suspension to improve ride comfort. Both simulations and hardware-in-the-loop testing results demonstrate the superiority of the proposed strategy: coordinated control maintains speed tracking error below 4%, reduces body acceleration by 16.1%, 11.9%, and 17.5%, and improves articulation angle oscillations by 12.6%, 14.6%, and 15.1% across scenarios, confirming the strategy’s enhanced performance over conventional single-loop control approaches.

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Publication Details

Journal
Machines
Published
2026-02-26
DOI
https://doi.org/10.3390/machines14030266
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Research on Coordinated Longitudinal–Vertical Control of Articulated Mining Trucks Using Extension Theory

Renkai Ding, C. Li, Xinying Li, Qing Ye
Machines
Vehicle Dynamics and Control Systems
article

Research on Coordinated Longitudinal–Vertical Control of Articulated Mining Trucks Using Extension Theory

Renkai Ding, C. Li, Xinying Li, Qing Ye
article en

Abstract

This research addresses the coupling issue between speed tracking and vertical posture in articulated unmanned mining trucks within unstructured environments. An extension theory-based coordinated control strategy is proposed, incorporating both articulation joint safety and vehicle stability. The control framework employs extension theory to classify operational modes based on articulation angle and velocity deviation. For longitudinal motion, active disturbance rejection control (ADRC) is adopted to mitigate the influence of varying payload mass and road slope on speed tracking performance. For vertical dynamics, a soft actor–critic (SAC) algorithm regulates active suspension to improve ride comfort. Both simulations and hardware-in-the-loop testing results demonstrate the superiority of the proposed strategy: coordinated control maintains speed tracking error below 4%, reduces body acceleration by 16.1%, 11.9%, and 17.5%, and improves articulation angle oscillations by 12.6%, 14.6%, and 15.1% across scenarios, confirming the strategy’s enhanced performance over conventional single-loop control approaches.

MachinesVol. 14(3)
Jiangsu University (CN), Nanjing Tech University (CN)
Affordable and clean energy
Openalex Percentile: Top 45%
Vehicle Dynamics and Control Systems
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