LPOA-MPC的车辆横纵向轨迹跟踪协同优化控制方法

Cooperative optimization of vehicle lateral and longitudinal trajectory tracking based on LPOA-MPC

关键词:  横纵联合  轨迹跟踪  模型预测控制  鹈鹕算法  参数整定

Key words:  transverse-longitudinal association  trajectory tracking  model predictive control  pelican algorithm  parameter tuning

摘要:

针对车辆模型预测控制(model predictive control,MPC)轨迹跟踪控制器中权重矩阵参数选取困难,导致车辆轨迹跟踪控制的稳定性与精度不足的问题,本文提出了一种融合多种机制的拉丁鹈鹕算法,用于优化控制车辆横、纵向联合模型预测轨迹跟踪控制器权重矩阵参数。首先,基于车辆单轨模型,分别设计了车辆横向MPC控制器和纵向的MPC上位控制器、基于加速度驱动力逆动力学模型的下位控制器；其次,针对鹈鹕算法(Pelican optimization algorithm,POA)在解空间内搜索效率低的问题,提出了一种拉丁鹈鹕算法(Latin Pelican optimization algorithm,LPOA),引入灰狼算法的等级狩猎机制重构POA的猎物定位模型,通过α鹈鹕引导策略提高算法收敛速度；同时,融入动态随机搜索策略,利用其重尾分布特性增强算法在迭代后期的局部极值逃逸能力；最后,基于LPOA的寻优能力,分别优化横、纵向MPC控制器权重矩阵参数,并通过Carsim和Simulink平台联合仿真验证所提出的横向、纵向以及横纵联合优化控制方法。研究表明,本文提出的LPOA-MPC控制器在车辆横向、纵向以及横纵联合控制中,均能够有效提高车辆轨迹跟踪控制稳定性与控制精度。

Abstract:

Aiming at the problem that it is difficult to select the weight matrix parameters in the vehicle model predictive control(MPC) trajectory tracking controller, which makes the stability and accuracy of the vehicle trajectory tracking control insufficient, this research propose a latin-pelican algorithm(LPOA) that integrates multiple mechanisms to optimize the weight matrix parameters of the vehicle lateral and longitudinal joint model predictive trajectory tracking controller. Firstly, the vehicle transverse MPC controller, the longitudinal MPC upper controller and the lower controller based on the acceleration-drive inverse dynamics model are designed respectively based on the vehicle single-track model;Secondly, a Latin Pelican Optimization Algorithm is proposed to improve the efficiency of the pelican algorithms searching in the solution space. The hierarchical hunting mechanism of the gray wolf algorithm is introduced to reconfigure the prey localization model of the POA, and the convergence speed of the algorithm is improved by the α-pelican guidance strategy. Thus, a dynamic stochastic search strategy is incorporated to enhance the algorithms ability to escape from local extremes in the late iteration by using its heavy-tailed distribution characteristics. Finally, the parameters of the horizontal and vertical MPC controller weight matrices are optimized using the optimization capability of LPOA; and the proposed horizontal, vertical, and horizontal-longitudinal joint optimization control methods are verified through co-simulation on CarSim and Simulink platforms. Results show that the LPOA-MPC controller proposed in this research can effectively improve the stability and accuracy of vehicle trajectory tracking control in horizontal, longitudinal and transverse-longitudinal joint control.