针对现有车辆横向控制中模型预测控制算法(MPC)的权重系数矩阵Q和R选取困难，导致车辆轨迹跟踪控制性能较差的问题，提出了一种基于多策略鹈鹕算法优化MPC权重系数矩阵Q和R的车辆轨迹跟踪控制方法。首先，建立简化的车辆动力学模型，并设计横向MPC控制器；其次，针对鹈鹕算法POA的局限性，提出了一种多种策略改进鹈鹕算法IPOA，引入社会学习策略和遗忘机制来建立鹈鹕个体之间的相互交互通道，提高了算法的收敛速度，同时，针对种群多样性不足和易陷入局部最优陷阱的问题，设计了一种带有反馈机制的动态自适应t分布变异算子，扩大了搜索空间，提高了后期的局部寻优能力；最后，利用IPOA优化MPC权重系数矩阵，并在Simulink和Carsim平台进行联合仿真。结果表明，提出的IPOA-MPC控制器能够有效地提高车辆跟踪参考轨迹的跟踪精度和稳定性，最大横向误差降低了60.74%，且优化后的控制器具有较强的泛化能力。

In response to the difficulty in selecting the weight matrices Q and R for the Model Predictive Control (MPC) algorithm in existing lateral vehicle control, which results in poor vehicle trajectory tracking performance, a vehicle trajectory tracking control method based on the multi-strategy Pelican Algorithm (POA) for optimizing the MPC weight matrices Q and R is proposed. Firstly, a simplified vehicle dynamics model is established, and a lateral MPC controller is designed. Secondly, to address the limitations of the Pelican Algorithm, an Improved Pelican Algorithm (IPOA) with multiple strategies is proposed. It introduces sociological learning strategies and a forgetting mechanism to establish interactive channels among Pelican individuals, enhancing the algorithm's convergence speed. Furthermore, to address the issues of insufficient population diversity and the tendency to fall into local optima, a dynamic adaptive t-distribution mutation operator with a feedback mechanism is designed to expand the search space and enhance the local optimization capability in later stages. Finally, the IPOA is used to optimize the MPC weight matrices, and joint simulation is conducted on the Simulink and Carsim platforms. Results show that the proposed IPOA-MPC controller effectively improves the accuracy and stability of vehicle tracking along reference trajectories, reducing the maximum lateral error by 60.74% and demonstrating strong generalization capabilities in the optimized controller.