基于安全距离的无人农机路径跟踪抗扰预测控制

黄臻臻; 孙金林; 丁世宏

doi:10.11959/j.issn.2096-6652.202508

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基于安全距离的无人农机路径跟踪抗扰预测控制

专栏：具身智能与特种机器人 | 更新时间：2025-06-10

- 基于安全距离的无人农机路径跟踪抗扰预测控制
- Anti-disturbance predictive control for path tracking of unmanned agricultural vehicles based on safety distance
- 智能科学与技术学报 2025年7卷第1期页码：86-97
- 作者机构：
  
  江苏大学电气信息工程学院，江苏镇江 212013
- 作者简介：
  
  [ "黄臻臻（2002- ），女，江苏大学电气信息工程学院硕士生，主要研究方向为无人农机系统、模型预测控制等。" ]
  [ "孙金林（1994- ），男，博士，江苏大学电气信息工程学院副教授，主要研究方向为无人农机系统、抗干扰控制、智能自适应控制与优化等。" ]
  [ "丁世宏（1983- ），男，博士，江苏大学电气信息工程学院院长、教授、博士生导师，主要研究方向为无人农机系统、滑模控制理论与应用等。" ]
- 基金信息：
  
  国家自然科学基金项目(62203189;62373170);江苏省自然科学基金项目(BK20220518);中国博士后科学基金项目(2024M751187);江苏大学高级人才基金项目(22JDG044)
- DOI：10.11959/j.issn.2096-6652.202508
  中图分类号： TP39
- 收稿日期：2024-08-02，
  
  修回日期：2024-10-18，
  
  纸质出版日期：2025-03-15
- 稿件说明：
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黄臻臻,孙金林,丁世宏.基于安全距离的无人农机路径跟踪抗扰预测控制[J].智能科学与技术学报,2025,07(01):86-97.

HUANG Zhenzhen,SUN Jinlin,DING Shihong.Anti-disturbance predictive control for path tracking of unmanned agricultural vehicles based on safety distance[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):86-97.
黄臻臻,孙金林,丁世宏.基于安全距离的无人农机路径跟踪抗扰预测控制[J].智能科学与技术学报,2025,07(01):86-97. DOI： 10.11959/j.issn.2096-6652.202508.

HUANG Zhenzhen,SUN Jinlin,DING Shihong.Anti-disturbance predictive control for path tracking of unmanned agricultural vehicles based on safety distance[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):86-97. DOI： 10.11959/j.issn.2096-6652.202508.

摘要

为提升无人农机在多障碍复杂作业场景下的路径跟踪性能，提出了一种基于安全距离的抗扰预测控制方案。该方案利用预测控制的自主学习能力，通过与作业场景的交互，实现了无人农机在复杂作业场景下的高效避障。首先，设计扩张状态观测器以精确估计农机系统中的未知扰动，并将其纳入非线性预测模型中，从而提升路径跟踪控制系统中状态预测的精度以及抗干扰性能。其次，设计无人农机参考点自动寻优算法，避免无人机在路径跟踪过程中过度转向。基于对数函数缩放构造新颖的避障惩罚项，确保在无人农机路径跟踪过程中与多重障碍始终保持预设的安全距离。最后，在成本函数中融入横向偏差与航向偏差信息，并结合避障惩罚项，通过在线求解非线性受限优化问题设计抗扰模型预测控制方案。仿真结果表明，该控制方案具有较高的路径跟踪精度，并能适应不同的安全距离和障碍物位置，实现有效避障。

Abstract

To enhance the path tracking performance of unmanned agricultural vehicles in complex working scenarios with multiple obstacles

this paper proposes an anti-disturbance predictive control scheme based on safe distance. This control scheme leverages the autonomous learning capabilities of predictive control to achieve efficient obstacle avoidance maneuvers for unmanned agricultural vehicles in complex operational scenarios through interaction with the working environment. Initially

an extended state observer is designed to accurately estimate the unknown disturbance within the agricultural vehicle system and incorporate it into the nonlinear predictive model

thereby improving the precision of state prediction and disturbance rejection in the path tracking control system. Subsequently

an automatic optimization algorithm for the reference point of the agricultural vehicle is designed to prevent excessive steering during path tracking. Then

a novel obstacle avoidance penalty term is constructed based on logarithmic function scaling to ensure that the agricultural vehicle maintains a preset safe distance from multiple obstacles during path tracking. Building on this

the cost function is integrated with lateral offset and heading offset information

combined with the obstacle avoidance penalty function

and an anti-disturbance model predictive control scheme is constructed by solving the nonlinear constrained optimization problem online. Simulation results demonstrate that the control scheme proposed in this paper has superior path tracking accuracy and can effectively avoid obstacles while adapting to different safe distance settings and obstacle positions.

关键词

Keywords

references

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