Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (12): 3717-3728.doi: 10.13229/j.cnki.jdxbgxb.20230167

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Intelligent vehicle trajectory tracking control based on curvature augmentation

Guo LIU1,2(),Jian XIONG1(),Xiu-jian YANG1,Yang-fan HE1   

  1. 1.Faculty of Transportation Engineering,Kunming University of Science and Technology,Kunming 650504,China
    2.City College,Kunming University of Science and Technology,Kunming 650051,China
  • Received:2023-02-24 Online:2024-12-01 Published:2025-01-24
  • Contact: Jian XIONG E-mail:liuguokust@126.com;xiebox@163.com

Abstract:

In order to improve tracking accuracy of intelligent vehicle under non-linear reference trajectories, a curvature augmentation model predictive control/propotional integral controller with front wheel angle compensation was proposed. First, based on tracking deviation vehicle model, model predictive control algorithm was designed by using curvature as augmentation state, and influence of curvature broadening was analyzed. Then, Lyapunov direct method was used to obtain predictive horizon that ensures algorithm stability, and system steady-state error was calculated. To eliminate steady-state lateral deviation, propotional integral controller was designed to compensate front wheel angle. Finally, simulation was conducted and results show that, the designed controller improves trajectory tracking accuracy and achieves better convergence rate, while ensuring stability and smoothness, and obtains good control effect under limit conditions.

Key words: automatic control technology, intelligent vehicle, trajectory tracking, curvature augmentation model predictive control, propotional integral control

CLC Number: 

  • TP273

Fig.1

Vehicle dynamic model and Frenet coordinate system"

Fig.2

Front wheel angle compensated by curvature augmentation"

Fig.3

Influence of vehicle speed on tracking deviation in prediction horizon and prediction step size selection"

Fig.4

Structure of curvature augmentation MPC+PI controller"

Table 1

Vehicle parameters for simulation"

车辆参数数值
车辆质量m/kg1723
前轴距质心的距离lf/m1.232
后轴距质心的距离lr/m1.468
车辆绕z轴的转动惯量I/(kg·m-24175
前轮侧偏刚度Cαf/(N·rad-1-66900
后轮侧偏刚度Cαr/(N·rad-1-62700
前轮转角约束umin,umax/rad-0.348?8,0.348?8
前轮转角变化量约束Δumin,Δumax/rad-0.017?4,0.017?4

Fig.5

Simulation results under Fishhook trajectory"

Table 2

Root mean square(RMS) and settling time of lateral deviation and heading angle deviation under Fishhook trajectory"

控制器横向偏差均方根/m航向角偏差均方根/rad稳定时间/s
预瞄MPC0.02930.001722.647
曲率增广MPC0.00580.000551.379
预瞄MPC+PI0.01580.001132.361
曲率增广MPC+PI0.00130.000491.22

Fig.6

Simulation results under double line shift trajectory"

Table 3

RMS of lateral deviation and heading angle deviation under double line shift trajectory"

控制器横向偏差均方根/m航向角偏差均方根/rad
预瞄MPC0.14410.0164
曲率增广MPC0.09320.0136
预瞄MPC+PI0.09470.0137
曲率增广MPC+PI0.05970.0087

Table 4

RMS of lateral deviation and heading angle deviation of curvature augmentation MPC + PI controller under different operating conditions"

工况横向偏差均方根/m航向角偏差均方根/rad
车速/(m·s-1附着系数
100.30.02240.0041
150.30.060.0113
250.60.10910.0203
300.80.12770.0234
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