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<p>Distributed-drive electric agricultural vehicle operating in field straight-line tasks is susceptible to working load disturbances and parameter uncertainties, which often result in heading deviation and lateral instability. To address this issue, a straight-line operation control mode is designed based on LQR yaw moment control integrated with disturbance observer compensation. Online estimation methods for key parameters, including the vehicle sideslip angle, longitudinal velocity, and vertical wheel loads, are developed. An LQR-based lateral controller incorporating model uncertainty and external disturbance compensation is constructed, and an online computation scheme for the target heading angle is realized using time-domain weighted least squares. Experimental results demonstrate that, compared with the original direct yaw moment control without the proposed algorithm, the heading angle control error is reduced by 80.13% on average and the lateral deviation is reduced by 62.27% on average under the condition of no manual steering intervention.</p>

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Straight-Line Path Tracking Control for Distributed-Drive Electric Agricultural Vehicle Based on LQR and Disturbance Observer

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Shaopeng Zhu,
  • Yize Lou,
  • Yuzhe Xu,
  • Shoulong Wang,
  • Xiang Zhao,
  • Haoxuan Jin,
  • Huipeng Chen

摘要

Distributed-drive electric agricultural vehicle operating in field straight-line tasks is susceptible to working load disturbances and parameter uncertainties, which often result in heading deviation and lateral instability. To address this issue, a straight-line operation control mode is designed based on LQR yaw moment control integrated with disturbance observer compensation. Online estimation methods for key parameters, including the vehicle sideslip angle, longitudinal velocity, and vertical wheel loads, are developed. An LQR-based lateral controller incorporating model uncertainty and external disturbance compensation is constructed, and an online computation scheme for the target heading angle is realized using time-domain weighted least squares. Experimental results demonstrate that, compared with the original direct yaw moment control without the proposed algorithm, the heading angle control error is reduced by 80.13% on average and the lateral deviation is reduced by 62.27% on average under the condition of no manual steering intervention.