上海交通大学学报

上海交通大学学报

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多策略鹈鹕算法优化的车辆轨迹跟踪MPC控制器（网络首发）

  

  1. 1.长安大学电子与控制工程学院；2.西安市智慧高速公路信息融合与控制重点实验室；3.长安大学信息工程学院
  • 基金资助:
    国家自然科学基金（52172379）； 中央高校基本科研业务费专项资金重点科研平台建设计划水平提升项目（300102324501）； 陕西省留学人员科技活动择优资助项目（2023001）； 西安市智慧高速公路信息融合与控制重点实验室(长安大学)开放基金项目(300102323502)

Multi-Strategy Pelican Algorithm Optimized MPC Controller for Vehicle Trajectory Tracking

  1. （1. School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China; 2.Xi’an Key Laboratory of Intelligent Expressway Information Fusion and Control, Xi’an 710064, China; 3. School of Information Engineering, Chang’an University, Xi’an 710064, China）

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摘要： 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。

关键词: 鹈鹕算法, 轨迹跟踪, 模型预测控制, 权重矩阵

Abstract: 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.

Key words: Pelican Algorithm, Trajectory tracking, Model predictive control, Weight matrix amnesic mechanism

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