Key Takeaways & Executive Findings
- •• A novel force-driven path tracking model using a linear time-varying tire model captures nonlinear tire dynamics for improved control. • The integrated H∞ and MPC controller with LMI-based constraints enhances robustness against modeling errors, parameter uncertainties, and curvature disturbances. • Simulation results show significant reductions in lateral deviation (up to 27.85%) and course angle deviation (up to 31.17%) under challenging conditions. • The proposed strategy offers a practical solution for high-speed and large-curvature path tracking in intelligent vehicles.
Abstract
Due to errors in vehicle dynamics modeling, uncertainty in model parameters, and disturbances from curvature, the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature conditions. Therefore, a path tracking robust control strategy based on force-driven H∞ and MPC is proposed. To fully exploit the nonlinear dynamics characteristics of tires, a force-driven state space model of a path tracking system based on a linear time-varying tire model is established; the H∞ and MPC methods are used to design a robust controller. Considering disturbance and system state constraints, the robust control constraint model based on LMI is established. Finally, the proposed controller is validated through joint simulations using CarSim and MATLAB. The results show that the maximum lateral deviation is reduced by 17.07%, and the maximum course angle deviation is reduced by 13.04% under large curvature disturbance conditions. The maximum lateral deviation is reduced by 27.85%, and the maximum course angle deviation is reduced by 31.17% under conditions of uncertain road adhesion coefficients. Based on the controller’s performance, the proposed controller effectively mitigates modeling errors, parameter uncertainties, and curvature disturbances.
1. Introduction
In recent years, significant achievements have been made in the field of intelligent vehicles [1, 2]. Path tracking control is a key technology for intelligent vehicles, aimed at accurately and stably following a reference path through steering control [3–5].
PID control, linear quadratic regulator (LQR), sliding mode control, model predictive control (MPC), and other theories have been applied to intelligent vehicle path tracking control, leading to the design of various controllers [4, 6–8]. The models utilized in the design of intelligent vehicle path tracking controllers primarily include the position and posture deviation model, vehicle kinematics model, and vehicle dynamics model. The deviation model combined with the PID method is employed to achieve accurate path tracking for intelligent vehicles [9, 10]. The structure of these controllers is simple, making them suitable for engineering applications. However, their generalizability is limited, and parameter tuning poses challenges for adaptation to different driving conditions. Controllers such as Stanley controllers are designed using the vehicle kinematics model and steering geometry [11]. These controllers perform well under simple road conditions and at low speeds. However, they inadequately consider vehicle dynamics information, resulting in poor tracking performance under variable curvature and high-speed conditions. To enhance vehicle safety and tracking performance under complex driving scenarios, control strategies utilizing vehicle dynamics models have been extensively investigated [12].
Vehicle modeling error, parameter uncertainty and external disturbance will lead to significant uncertainty in control system under extreme conditions, which will seriously change the system stability and control performance of path tracking controller [13, 14]. Sliding mode control, H∞ and active disturbance rejection control can achieve suppression for disturbance within a certain range and effectively improve the robustness of control system. The sliding mode control method has the advantages of fast response and insensitivity to parameter changes and disturbances. Therefore, it is used to achieve path tracking robust control [15, 16]. However, sliding mode control suffers from chattering issue. H∞ control has been widely studied and applied in the field of vehicle path tracking by solving the optimal feedback control gain that satisfies H∞ performance [17–19]. Wang et al. [20] studied the control method based on H∞ for vehicle parameter uncertainty and actuator failure. Hu et al. [21] considered the measurement difficulty of vehicle lateral speed, model uncertainty, and external disturbances, and the H∞ output...
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Qiangqiang Yao, Yiheng Shi, Peng Hang, Ying Tian (2025). Path Tracking Robust Control Strategy for Intelligent Vehicle Based on Force-Driven with MPC and H∞. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01294-8
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a novel path tracking robust control strategy for intelligent vehicles that integrates force-driven modeling with H∞ and MPC, effectively handling modeling errors, parameter uncertainties, and curvature disturbances.
How does the proposed controller improve path tracking performance?
By using a linear time-varying tire model and LMI-based constraints, the controller reduces maximum lateral deviation by up to 27.85% and course angle deviation by up to 31.17% under uncertain road adhesion conditions.
What methods are combined in the proposed control strategy?
The strategy combines H∞ control and Model Predictive Control (MPC) within a force-driven state space model, using Linear Matrix Inequalities (LMIs) to handle constraints and disturbances.
How was the controller validated?
The controller was validated through joint simulations using CarSim and MATLAB, demonstrating significant improvements under large curvature and uncertain road adhesion conditions.
What are the practical implications of this research?
The proposed control strategy enhances the stability and accuracy of intelligent vehicle path tracking in high-speed and large-curvature scenarios, contributing to safer autonomous driving.
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