基于MFAPC的无人驾驶汽车路径跟踪方法

doi:10.19678/j.issn.1000-3428.0052439

摘要/Abstract

摘要：

为提高无人驾驶汽车转向系统对目标路径的跟踪精度,提出一种新的无人驾驶汽车路径跟踪方法。介绍基于跟踪预瞄点的无人驾驶汽车横向控制方案,给出系统的动态线性化数据模型及其多步预测方程,并采用最小二乘法推导出伪梯度向量的估计和预测方程。结合无模型自适应控制与预测控制的优点,能够通过滚动优化策略进行反复的在线计算,从而得到较好的动态性能。基于CarSim/Simulink联合仿真平台,在车速分别为5 m/s、20 m/s时进行验证,结果表明,与基于车辆动力学的MPC方案相比,该方法具有更好的跟踪效果。

关键词: 无人驾驶, 路径跟踪, 预瞄策略, 无模型自适应预测控制, 联合仿真

Abstract:

In order to improve the tracking precision of the unmanned vehicle steering system to the target path,this paper proposes a new unmanned vehicle path tracking method.It introduces the lateral control scheme of unmanned vehicle based on tracking preview point,and gives the dynamic linearized data model of the system and its multi-step prediction equation.The estimation and prediction model of pseudo-gradient vectors is derived by using the method of least squares.Adopting the advantages of model-free adaptive predictive control,it can perform repeated online calculations through rolling optimization strategies,so as to obtain better dynamic performance.Based on the CarSim/Simulink co-simulation platform,the method is verified when the vehicle speed is 5 m/s and 20 m/s.Results show that compared with the MPC scheme based on vehicle dynamics,this proposed method has better tracking performance.

Key words: unmanned, path tracking, preview strategy, Model-Free Adaptive Predictive Control(MFAPC), co-simulation

段建民, 马学峥, 柳新. 基于MFAPC的无人驾驶汽车路径跟踪方法[J]. 计算机工程, 2019, 45(6): 6-11.

Jianmin DUAN, Xuezheng MA, Xin LIU. Path Tracking Method of Unmanned Vehicle Based on MFAPC[J]. Computer Engineering, 2019, 45(6): 6-11.

http://www.ecice06.com/CN/Y2019/V45/I6/6

图/表 12

参考文献 19

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符号	符号意义
X,O,Y	大地坐标系
Ex	与当前时刻预瞄点的X轴差值
Ey	与当前时刻预瞄点的Y轴差值
δ	车辆航向偏角(航偏角)
θ	航偏角与车辆-预瞄点连线和X轴成角的差角

符号	符号意义
X,O,Y	大地坐标系
Ex	与当前时刻预瞄点的X轴差值
Ey	与当前时刻预瞄点的Y轴差值
δ	车辆航向偏角(航偏角)
θ	航偏角与车辆-预瞄点连线和X轴成角的差角

参数	取值
车辆质量/kg	m=1 723
绕z轴转动惯量/(kg·m^-²)	I=4 175
车身尺寸/m	L=4,W=1.988
轴距/m	a=1.232, b=1.468
轮胎侧偏刚度/(N·rad^-¹)	Cf=66 900,Cr=62 700

参数	取值
车辆质量/kg	m=1 723
绕z轴转动惯量/(kg·m^-²)	I=4 175
车身尺寸/m	L=4,W=1.988
轴距/m	a=1.232, b=1.468
轮胎侧偏刚度/(N·rad^-¹)	Cf=66 900,Cr=62 700

参数	取值
预测时域	N_p=35
控制时域	N_u=1
滑动时间窗口长度	L=5
权重因子	λ=0.85
伪梯度向量初始值	?_L(0,:)=0.2
估计算法正定矩阵	P₁(-1)=10I
预报算法正定矩阵	P₂(-1)=100I
最小二乘距离因子	a(0)=b(0)=0.95
遗忘因子参数	a₀=b₀=0.99
时变参数矩阵阶数	n=3
时变参数矩阵初始值	Λ(0)=ones(3L,L)

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