PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 28, 2026Machines0 citationsOpen Access

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

View Full Paper
XLXinying LiCLC. LiQYQing Ye

Key Points

  • This research aims to improve speed tracking and vertical posture control in articulated mining trucks in unstructured environments.
  • Developed a coordinated control strategy based on extension theory.
  • Employed active disturbance rejection control (ADRC) for longitudinal motion.
  • Utilized a soft actor–critic (SAC) algorithm for vertical dynamics and active suspension regulation.
  • Conducted simulations and hardware-in-the-loop testing to evaluate performance.
  • Maintained speed tracking error below 4%.
  • Reduced body acceleration by 16.1%, 11.9%, and 17.5% across scenarios.
  • Improved articulation angle oscillations by 12.6%, 14.6%, and 15.1% compared to conventional methods.

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69a286a70a974eb0d3c01bd8https://doi.org/10.3390/machines14030266
Ask AI
Helpful
Bookmark
Share
View Full Paper