Scholarlink

<p>Autonomous vehicles operating in specialized domains, such as smart agriculture, face significant control challenges that are not prevalent on structured roads. For high center-of-gravity (HCG) agricultural machinery, navigating high-curvature paths on non-rigid terrains like farmland poses a dual threat to path tracking accuracy and vehicle stability. This paper addresses this challenge by proposing a novel hierarchical control strategy. We develop a vehicle stability model that uniquely incorporates a tire-soil sinkage model, enabling real-time calculation of a dynamic rollover threshold specific to deformable surfaces. The control framework features a multimodal Model Predictive Controller (MPC) for agile and precise path tracking with a four-wheel steering (4WS) system. For stability, a fuzzy logic controller adaptively modulates an artificial potential field to regulate vehicle speed, ensuring the vehicle remains within its dynamic stability limits without unnecessarily compromising tracking performance. The primary contribution is this synergistic integration of a soil-interaction model into a predictive control framework, closing a critical gap in off-road vehicle control. The strategy’s effectiveness is validated through simulations and extensive field experiments on an intelligent soil-sampling robot, demonstrating superior performance in both path-following fidelity and rollover prevention compared to baseline approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A fuzzy potential field-based MPC strategy for stable path tracking of high-gravity 4WS vehicles on non-rigid pavements

  • 期刊论文

  • 发表日期：

    2026年2月2日

查看全文
  • Xu Yang,
  • Xinxian Deng,
  • Yu Lei,
  • Shengzhi Zhong,
  • Bin Huang

摘要

Autonomous vehicles operating in specialized domains, such as smart agriculture, face significant control challenges that are not prevalent on structured roads. For high center-of-gravity (HCG) agricultural machinery, navigating high-curvature paths on non-rigid terrains like farmland poses a dual threat to path tracking accuracy and vehicle stability. This paper addresses this challenge by proposing a novel hierarchical control strategy. We develop a vehicle stability model that uniquely incorporates a tire-soil sinkage model, enabling real-time calculation of a dynamic rollover threshold specific to deformable surfaces. The control framework features a multimodal Model Predictive Controller (MPC) for agile and precise path tracking with a four-wheel steering (4WS) system. For stability, a fuzzy logic controller adaptively modulates an artificial potential field to regulate vehicle speed, ensuring the vehicle remains within its dynamic stability limits without unnecessarily compromising tracking performance. The primary contribution is this synergistic integration of a soil-interaction model into a predictive control framework, closing a critical gap in off-road vehicle control. The strategy’s effectiveness is validated through simulations and extensive field experiments on an intelligent soil-sampling robot, demonstrating superior performance in both path-following fidelity and rollover prevention compared to baseline approaches.