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<p>In this paper, a longitudinal and lateral coupled trajectory-tracking controller for autonomous vehicles is proposed. First, a trajectory-tracking model with system parameter uncertainty and dynamic nonlinear coupling characteristics is modeled and analyzed. We design a triple-step nonlinear controller and introduce a parameter adaption law to address the nonlinear coupling between the longitudinal and lateral dynamics of the vehicle. This approach not only reduces CPU usage but also effectively overcomes disturbances caused by system parameter uncertainty. We evaluated the controller through hardware-in-the-loop (HiL) tests. The experimental results show that the proposed controller achieves higher precision and delivers smoother, more stable performance compared to the linear quadratic regulator (LQR), linear parameter-varying (LPV) H∞ robust and adaptive model predictive control (AMPC) methods.</p>

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Trajectory-Tracking Control of Autonomous Vehicles with Dynamic Nonlinear Coupling Characteristics and Parameter Uncertainty

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Lun Li,
  • Deping Wang,
  • Zhicheng Chen,
  • Bing Zhu,
  • Jian Zhao,
  • Haiqiao Li,
  • Jiayi Han,
  • Dongjian Song,
  • Peixing Zhang

摘要

In this paper, a longitudinal and lateral coupled trajectory-tracking controller for autonomous vehicles is proposed. First, a trajectory-tracking model with system parameter uncertainty and dynamic nonlinear coupling characteristics is modeled and analyzed. We design a triple-step nonlinear controller and introduce a parameter adaption law to address the nonlinear coupling between the longitudinal and lateral dynamics of the vehicle. This approach not only reduces CPU usage but also effectively overcomes disturbances caused by system parameter uncertainty. We evaluated the controller through hardware-in-the-loop (HiL) tests. The experimental results show that the proposed controller achieves higher precision and delivers smoother, more stable performance compared to the linear quadratic regulator (LQR), linear parameter-varying (LPV) H∞ robust and adaptive model predictive control (AMPC) methods.