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<p>This work presents the development of a hierarchical lateral control strategy for intelligent vehicles, aimed at addressing the degradation of trajectory tracking accuracy and lateral stability caused by time-varying tire cornering stiffness—changes that typically arise under nonlinear tire conditions and dynamic load transfer scenarios. A two-layer estimation architecture is designed to enable real-time and accurate identification of tire cornering stiffness: the upper layer leverages a Lyapunov-based sliding mode observer to estimate lateral force and vehicle sideslip angle, while the lower layer integrates these estimated states into an adaptive Kalman filter (AKF) with noise covariance updating rules. This adaptive rule allows the AKF to adapt to nonlinear operating conditions where tire stiffness varies significantly, ensuring reliable stiffness identification results. The identified cornering stiffness values are then incorporated into a Linear Quadratic Regulation (LQR) controller to optimize front-wheel steering input, thereby enhancing the controller’s robustness against model uncertainties induced by stiffness changes. The performance of the proposed LQR with Stiffness Identification (LSI-LQR) controller is validated through CarSim–Simulink co-simulations and hardware-in-the-loop (HIL) experiments. Test scenarios include asphalt pavements with high (0.85) and low (0.45) adhesion coefficients, multi-lane change maneuvers, and vehicle speeds of 72&#xa0;km/h and 54&#xa0;km/h. Experimental results show that, compared with feedforward LQR and feedforward + preview LQR controllers, the LSI-LQR controller improves tracking accuracy by an average of 35.997% under high-adhesion conditions and 65.87% under low-adhesion conditions, It is verified that the LSI-LQR controller can effectively guarantee both trajectory tracking accuracy and lateral stability of the vehicle. The main contribution of this research lies in the integration of real-time tire cornering stiffness identification into LQR-based lateral control, which bridges the gap between fixed stiffness assumptions and actual tire dynamics. This work provides a practical and reliable solution for motion control in automated driving systems, with strong potential for application in real-world intelligent vehicle platforms.</p>

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Intelligent vehicle tracking control with real-time tire cornering stiffness identification

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Yuhua Zhang,
  • Yuying Fang,
  • Kaichen Cui,
  • Pengwei Wang,
  • Yuchen Tan,
  • Chengyi Yu,
  • Song Gao,
  • Binbin Sun

摘要

This work presents the development of a hierarchical lateral control strategy for intelligent vehicles, aimed at addressing the degradation of trajectory tracking accuracy and lateral stability caused by time-varying tire cornering stiffness—changes that typically arise under nonlinear tire conditions and dynamic load transfer scenarios. A two-layer estimation architecture is designed to enable real-time and accurate identification of tire cornering stiffness: the upper layer leverages a Lyapunov-based sliding mode observer to estimate lateral force and vehicle sideslip angle, while the lower layer integrates these estimated states into an adaptive Kalman filter (AKF) with noise covariance updating rules. This adaptive rule allows the AKF to adapt to nonlinear operating conditions where tire stiffness varies significantly, ensuring reliable stiffness identification results. The identified cornering stiffness values are then incorporated into a Linear Quadratic Regulation (LQR) controller to optimize front-wheel steering input, thereby enhancing the controller’s robustness against model uncertainties induced by stiffness changes. The performance of the proposed LQR with Stiffness Identification (LSI-LQR) controller is validated through CarSim–Simulink co-simulations and hardware-in-the-loop (HIL) experiments. Test scenarios include asphalt pavements with high (0.85) and low (0.45) adhesion coefficients, multi-lane change maneuvers, and vehicle speeds of 72 km/h and 54 km/h. Experimental results show that, compared with feedforward LQR and feedforward + preview LQR controllers, the LSI-LQR controller improves tracking accuracy by an average of 35.997% under high-adhesion conditions and 65.87% under low-adhesion conditions, It is verified that the LSI-LQR controller can effectively guarantee both trajectory tracking accuracy and lateral stability of the vehicle. The main contribution of this research lies in the integration of real-time tire cornering stiffness identification into LQR-based lateral control, which bridges the gap between fixed stiffness assumptions and actual tire dynamics. This work provides a practical and reliable solution for motion control in automated driving systems, with strong potential for application in real-world intelligent vehicle platforms.