With the rapid development and implementation of autonomous vehicles (AVs), the simultaneous and accurate trajectory tracking problem for such AVs has become a popular research topic. This paper proposes a comprehensive linear time-varying model predictive controller (LTV-MPC) design for a type of AV, aiming to achieve good trajectory tracking in a practical driving scenario. First, a two-degree-of-freedom kinematic model of an AV is established. Next, an error model of the AV’s trajectory tracking system is constructed using linear time-varying theory. A successive linearization is introduced to linearize the nonlinear tracking error model, and a quadratic programming optimization problem is then formulated. Thus, the control sequence for this AV is incorporated into the predictive control framework, and the desired controller can be solved with a relatively higher computational efficiency and lower computational cost. Finally, the effectiveness and performance of the proposed controller are validated via a comparison of simulations conducted using MATLAB software and experiments conducted using a self-established test platform. The results demonstrate that the proposed LTV-MPC method can track the prescribed reference road trajectories with high precision and stability for an AV under various driving conditions.

随着自动驾驶汽车 (AV) 的快速发展和实施，此类 AV 的同步且准确的轨迹跟踪问题已成为热门研究课题。本文提出了一种针对某类自动驾驶汽车的综合线性时变模型预测控制器 (LTV-MPC) 设计，旨在在实际驾驶场景中实现良好的轨迹跟踪。首先，建立AV的二自由度运动学模型。接下来，使用线性时变理论构建自动驾驶汽车轨迹跟踪系统的误差模型。引入逐次线性化来线性化非线性跟踪误差模型，然后制定二次规划优化问题。因此，该 AV 的控制序列被合并到预测控制框架中，并且可以以相对较高的计算效率和较低的计算成本来求解期望的控制器。最后，通过比较使用 MATLAB 软件进行的模拟和使用自建测试平台进行的实验，验证了所提出控制器的有效性和性能。结果表明，所提出的 LTV-MPC 方法可以在各种驾驶条件下为 AV 跟踪指定的参考道路轨迹，具有高精度和稳定性。