车辆电动化与智能化的不断发展导致自动驾驶车辆的研究已逐步成为当今热点领域。路径跟踪控制作为自动驾驶最重要的一环，对于实现自动驾驶功能具有重要的意义。其中，前轮转角是直接影响自动驾驶车辆的运动控制性能的主要因素之一。然而，传统的定前轮转角约束控制器不能实时适应不同的工况变化，进而限制了智能车辆在预定路径上的跟踪精度和行驶稳定性。本文结合车辆的动力学特性和模型预测控制（Model Predictive Control，MPC）算法，提出基于车辆稳定性的前轮转角约束自适应MPC路径跟踪控制策略，该策略能够在不同的工况下，保障车辆的运动性能，从而显著提高自动驾驶车辆在预定路径上的跟踪性能和行驶稳定性。本文主要研究内容为：

（1）采用魔术轮胎公式建立轮胎模型，分析轮胎横向和纵向的动力学特性，基于魔术轮胎模型，构建二自由度车辆动力学模型和三自由度车辆动力学模型分别作为稳定性分析参考模型和路径跟踪参考模型。为确保车辆动力学模型的准确性，利用实车的实际测量参数和力学理论计算相结合来配置模型参数。

（2）针对车辆前轮转角不能实时适应不同工况的问题，设计基于车辆动力学和稳定性前轮转角约束条件：依据轮胎动力学模型，分析轮胎的纵向力，结合车辆与前轮转角的动力学关系，设定前轮转角的动力学约束条件；从车辆稳定性角度出发，设定前轮转角的稳定性约束条件，避免因转角过大或变化过快导致车辆失稳。综合考虑转角的动力学约束和稳定性约束，设定前轮转角自适应约束条件。结合MPC控制器，根据车辆实时状态动态调整前轮转角，实现前轮转角的约束自适应。

（3）针对车辆路径跟踪过程中跟踪精度低且行驶不稳定的问题，设计MPC路径跟踪控制器，基于GA-PSO优化算法优化MPC的重要时域参数，并与未优化的MPC控制器进行仿真对比。结果表明，优化后MPC控制器的横向和航向误差最大值分别降低79.09%和22.95%。再结合所设定的前轮转角约束条件和MPC滚动优化特性，设计基于转角约束自适应MPC路径跟踪控制策略，动态调整前轮转角的约束，从而在不同的工况下实现高精度的路径跟踪和行驶稳定性，同时研究每一时刻的车辆参数输出和前轮转角对路径跟踪控制的影响。

（4）针对所设计路径跟踪策略控制性能的验证问题，搭建CarSim/Simulink联合仿真平台，配置相关参数，并在多种工况下进行仿真验证，分析该控制策略在路径跟踪精度和稳定性方面的表现；在Simulink-ROS分层架构的基础上搭建实车试验平台，设计完整的实车试验流程，并进行路径跟踪控制策略实车试验，进一步验证控制策略在实际控制中的有效性。结果表明，所设计控制器的横向位移、横摆角、质心侧偏角和横摆角速度的均方根值均得到改善。

The continuous advancement of vehicle electrification and intelligentization has made autonomous driving a prominent research focus in recent years. As one of the most critical components of autonomous driving systems, path-tracking control plays a vital role in achieving reliable autonomous vehicle functionality. However, conventional fixed front-wheel angle constraint controllers fail to adapt to varying operational conditions in real time, limiting tracking accuracy and driving stability for intelligent vehicles along predefined paths.To address this limitation, this study integrates vehicle dynamics characteristics with a Model Predictive Control (MPC) algorithm, proposing a stability-based adaptive MPC path-tracking control strategy with front-wheel angle constraints. The proposed strategy dynamically adjusts to diverse driving conditions, ensuring consistent motion performance and substantially improving both path-tracking precision and driving stability for autonomous vehicles.The main research contents of this thesis are as follows:

(1) The Magic Formula Tire model was employed to establish the tire model, analyzing both lateral and longitudinal tire dynamics characteristics. Based on the Magic Formula Tire model, two-degree-of-freedom and three-degree-of-freedom vehicle dynamics models were constructed to serve as the reference models for stability analysis and path tracking respectively. To ensure the accuracy of the vehicle dynamics models, the model parameters were configured through a combination of actual measured parameters from real vehicles and mechanical theory calculations.

(2) To address the problem that the front-wheel steering angle cannot adapt to different working conditions in real time, a steering angle constraint condition was designed based on vehicle dynamics and stability requirements: Based on the tire dynamics model, the longitudinal tire forces were analyzed and combined with the dynamic relationship between the vehicle and front-wheel steering angle to establish dynamic constraints for the steering angle. From the perspective of vehicle stability, the minimum and maximum values of the steering angle were determined, along with stability constraints, to prevent vehicle instability caused by excessive steering angles or overly rapid steering changes. By comprehensively considering both the dynamic constraints and stability constraints, adaptive steering angle constraints were established. Within the MPC controller framework, the front-wheel steering angle was dynamically adjusted in real time according to the vehicle's actual state, thereby achieving adaptive steering angle control.

(3) To address the issues of low tracking accuracy and unstable driving performance in vehicle path tracking, this study designed an MPC path tracking controller with its critical time-domain parameters optimized using a GA-PSO algorithm, followed by comparative simulations with conventional MPC controllers. Results demonstrate that the optimized MPC controller achieves 79.09% and 22.95% reductions in maximum lateral and heading errors, respectively. By integrating predefined front-wheel steering angle constraints with MPC's receding horizon optimization, an adaptive MPC path tracking control strategy was developed to dynamically adjust steering angle constraints, thereby ensuring high-precision path tracking and enhanced driving stability across various operating conditions, while simultaneously investigating the influence of real-time vehicle parameter outputs and steering angles on control performance.

(4) To validate the control performance of the designed path tracking strategy, a CarSim/Simulink co-simulation platform was established with relevant parameters configured. Extensive simulation tests were conducted under various operating conditions to analyze the strategy's performance in terms of path tracking accuracy and stability. Furthermore, based on the Simulink-ROS hierarchical architecture, a physical vehicle test platform was constructed. A complete vehicle testing procedure was designed and implemented to conduct real-world path tracking control experiments, thereby further verifying the effectiveness of the control strategy in practical applications. The results demonstrate that the designed controller achieves improvements in the root mean square values of lateral displacement, yaw angle, sideslip angle, and yaw rate.

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