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To address the stability control requirements of autonomous shuttle coach under complex operating conditions, this paper proposes a longitudinal and lateral cooperative hierarchical control strategy for four-wheel steering (4WS) vehicles based on phase plane stability judgment and an instability quantification function. First, the improved phase plane stability domain is used to accurately determine the vehicle stability state, and an instability quantification function integrating state trajectory trends, steering characteristics, and manipulation inputs is adopted to achieve continuous and precise description of the stability state. Furthermore, a gradient-progressive hierarchical controller is designed: PID control is used in the stable phase, LQR control combined with vehicle speed adjustment in the critical phase, and MIMO-MFAC (Multi-Input Multi-Output Model-Free Adaptive Control) for longitudinal and lateral cooperative intervention in the unstable phase. Experimental verification shows that this strategy enables the controlled vehicle to quickly regain stability under extreme conditions and exhibits strong robustness against system parameter disturbances, providing an effective theoretical method and technical support for improving the active safety of vehicles.

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A Hierarchical Stability Control Strategy for Autonomous 4WS Coach Using Instability Quantification

  • 会议论文

  • 发表日期：

    2026年

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  • Ruijian Zhang,
  • Chang Chen,
  • Ren Li,
  • Haipeng Lai,
  • Yuqing Niu,
  • Lulu Cai

摘要

To address the stability control requirements of autonomous shuttle coach under complex operating conditions, this paper proposes a longitudinal and lateral cooperative hierarchical control strategy for four-wheel steering (4WS) vehicles based on phase plane stability judgment and an instability quantification function. First, the improved phase plane stability domain is used to accurately determine the vehicle stability state, and an instability quantification function integrating state trajectory trends, steering characteristics, and manipulation inputs is adopted to achieve continuous and precise description of the stability state. Furthermore, a gradient-progressive hierarchical controller is designed: PID control is used in the stable phase, LQR control combined with vehicle speed adjustment in the critical phase, and MIMO-MFAC (Multi-Input Multi-Output Model-Free Adaptive Control) for longitudinal and lateral cooperative intervention in the unstable phase. Experimental verification shows that this strategy enables the controlled vehicle to quickly regain stability under extreme conditions and exhibits strong robustness against system parameter disturbances, providing an effective theoretical method and technical support for improving the active safety of vehicles.