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<p>This article proposes a novel adaptive sliding mode control method specifically designed to address the path tracking problems of autonomous ground vehicles (AGVs) under complex conditions such as time-varying delays, tire stiffness uncertainties, and external disturbances. To resolve the singularities caused by incomplete vehicle constraints, a singular vehicle model that comprehensively considers practical factors was established. Subsequently, a state observer and an integral sliding mode surface incorporating a singular matrix were constructed, leading to the development of a path tracking controller that is insensitive to parameter uncertainties and disturbances. Sufficient conditions ensuring the admissibility of singular vehicle systems were derived using Lyapunov functions and Jensen’s inequality. Experimental simulations validate the effectiveness of this method, and superiority is demonstrated by comparing it with existing approaches.</p>

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Observer-based adaptive path tracking control for autonomous ground vehicle with singularities and time-varying delays

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Shou-Wu Zhang,
  • Li Ma,
  • Qing Li,
  • Heng Wang

摘要

This article proposes a novel adaptive sliding mode control method specifically designed to address the path tracking problems of autonomous ground vehicles (AGVs) under complex conditions such as time-varying delays, tire stiffness uncertainties, and external disturbances. To resolve the singularities caused by incomplete vehicle constraints, a singular vehicle model that comprehensively considers practical factors was established. Subsequently, a state observer and an integral sliding mode surface incorporating a singular matrix were constructed, leading to the development of a path tracking controller that is insensitive to parameter uncertainties and disturbances. Sufficient conditions ensuring the admissibility of singular vehicle systems were derived using Lyapunov functions and Jensen’s inequality. Experimental simulations validate the effectiveness of this method, and superiority is demonstrated by comparing it with existing approaches.