角模块架构智能电动汽车路径跟踪鲁棒预测控制  

Robust Predictive Control for Path Tracking of Intelligent Electric Vehicle Based on Wheel Corner Module

作　　者：张利鹏[1] 穆建华 王建涛 张俊达 祝军军 ZHANG Li-peng;MU Jian-hua;WANG Jian-tao;ZHANG Jun-da;ZHU Jun-jun(School of Vehicle and Energy,Yanshan University,Qinhuangdao 066004,Hebei,China)

机构地区：[1]燕山大学车辆与能源学院,河北秦皇岛066004

出　　处：《中国公路学报》2024年第1期241-254,共14页China Journal of Highway and Transport

基　　金：国家自然科学基金项目(52272407)。

摘　　要：集成四轮独立驱动/独立转向系统的车轮角模块可以使整车具有优越的低速机动性和高速稳定性,是智能车辆的理想载体,但过多的转角/转矩控制输入增加了整车控制难度,实际行驶中的不确定性干扰也严重影响车辆控制鲁棒性,常用的标准模型预测控制无法处理这些不确定问题。为了提高角模块架构智能电动汽车路径跟踪控制鲁棒性,提出了基于Tube的鲁棒模型预测控制(Tube-RMPC)方法。搭建了整车动力学模型和路径跟踪模型,分析了车辆行驶过程中的不确定性,建立了干扰有界线性时变不确定性模型系统,构造了名义系统模型预测控制最优化问题,提出了一种能够应用于路径跟踪控制系统的具有较低系统保守性的鲁棒不变集计算方法,结合闭环系统反馈构建了鲁棒模型预测控制策略,基于MATLAB/Simulink和CarSim联合仿真与硬件在环测试平台验证了所提控制策略的有效性和实时性。研究结果表明:在存在自身参数不确定及外界路面附着系数不确定性扰动状态下,所提出的Tube-RMPC相对于标准模型预测控制将横向位移偏差最大值分别降低了30.2%和48.4%,质心侧偏角最大值分别降低了31.6%和7.8%,有效提高了车辆跟踪精度及稳定性。所提出控制策略在保证控制精度的同时具有良好的鲁棒性,对其他智能汽车的控制系统设计具有重要参考价值。The wheel angle module integrated with the four-wheel independent drive/steering system can make the whole vehicle have excellent low-speed mobility and high-speed stability,and is an ideal carrier for intelligent vehicles,but too many angle/torque control inputs increase the difficulty of the whole vehicle control,and the uncertainty interference in actual driving also seriously affects the robustness of vehicle control,the common standard model predictive control cannot deal with these uncertainties.In order to improve the robustness of path tracking control for intelligent electric vehicle based on wheel corner module,a tube-based robust model predictive control(Tube-RMPC)method was proposed.The vehicle dynamics model and the path tracking model were built to analyze the uncertainty problems in the vehicle driving process and established the disturbance bounded linear time-varying uncertainty model system,constructs the nominal system model predictive control optimization problem,and proposes a robust invariant set calculation method that can be applied to the path tracking control system.A robust model predictive control strategy was constructed based on closed-loop system feedback.Based on MATLAB/Simulink and CarSim joint simulation and hardware-in-the-loop test platform,the effectiveness and real-time of the proposed control strategy were verified.The results show that,under the presence of uncertainties and disturbances from both the vehicle's own parameter uncertainties and external road surface adhesion coefficient uncertainties,the proposed Tube-RMPC reduces the maximum lateral displacement error by 30.2%and 48.4%,and the maximum lateral tilt angle by 31.6%and 7.8%,respectively,compared to the standard model predictive control.This effectively improves the vehicle tracking accuracy and stability.The proposed control strategy has good robustness while ensuring control accuracy,and has important reference value for the design of intelligent vehicle control system.

关 键 词：汽车工程 路径跟踪 鲁棒预测控制 四轮独立转向 直接横摆力矩控制 智能汽车 

分 类 号：U461.1[机械工程—车辆工程]