吉林大学学报(工学版) ›› 2020, Vol. 50 ›› Issue (5): 1545-1555.doi: 10.13229/j.cnki.jdxbgxb20190535

• 车辆工程·机械工程 •    

基于车辆状态估计的商用车ESC神经网络滑模控制

李静1(),石求军1,洪良2,刘鹏1   

  1. 1.吉林大学 汽车仿真与控制国家重点实验室, 长春 130022
    2.一汽解放汽车有限公司 商用车开发院,长春 130011
  • 收稿日期:2019-05-30 出版日期:2020-09-01 发布日期:2020-09-16
  • 作者简介:李静(1974-),男,教授,博士生导师.研究方向:汽车地面系统分析设计与控制.E-mail:l_jing@jlu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2018YFB0105900)

Commercial vehicle ESC neural network sliding mode control based on vehicle state estimation

Jing LI1(),Qiu-jun SHI1,Liang HONG2,Peng LIU1   

  1. 1.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
    2.Commercial Vehicle Development Institute, FAW Jiefang Automotive CO. LTD. , Changchun 130011, China
  • Received:2019-05-30 Online:2020-09-01 Published:2020-09-16

摘要:

针对商用车电子稳定性控制系统(ESC)中纵向车速、侧向车速、质心侧偏角等部分车辆状态参数难以直接获得、车辆系统传感器过程噪声一般为时变且未知等问题,提出了自适应容积卡尔曼滤波(ADCKF)算法,将标准容积卡尔曼滤波(CKF)算法与次优Sage-Husa噪声估计算法结合在一起,对部分车辆状态参数进行实时在线估计;然后根据商用车ESC的控制需求,并考虑建模不确定性和外界扰动,提出了商用车ESC径向基(RBF)神经网络滑模控制算法,利用径向基神经网络对干扰项进行估计。最后,通过MATLAB/Simulink与TruckSim联合仿真,对上述算法进行仿真验证。仿真结果表明:ADCKF算法对商用车的车辆部分状态参数估计较为精确,基于车辆状态估计的商用车ESC神经网络滑模算法控制效果良好,能满足商用车ESC控制需求。

关键词: 车辆工程, 容积卡尔曼滤波, 自适应, Sage-Husa噪声估计器, 滑模控制, 径向基神经网络

Abstract:

For the Electronic Stability Controller(ESC) of commercial vehicle, the partial parameters such as longitudinal velocity, lateral velocity, and sideslip angle are difficult to obtain directly, and the sensor process noise of the vehicle system is generally time-varying and unknown. To solve the problems, an Adaptive Cubature Kalman Filter(ADCKF) algorithm was proposed to estimate the vehicle state parameters. First, the standard Cubature Kalman Filter(CKF) algorithm was combined with the suboptimal Sage-Husa estimation algorithm to estimate the parameters of vehicle. Then, according to the ESC control requirements and considering the modeling uncertainty and external disturbances, the commercial vehicle ESC control of Radial Basis Function(RBF) neural network Sliding Mode Control(SMC) algorithm was proposed. Finally, the disturbances were estimated by RBF neural network. The co-simulation results of MATLAB/Simulink and TruckSim show that the ADCKF algorithm is accurate in estimating vehicle state parameters, and the RBF neural network SMC based on vehicle state estimation of commercial vehicle ESC has good control effect.

Key words: vehicle engineering, cubature Kalman filter, adaptive, Sage-Husa noise estimator, sliding mode control, radical basis neural network

中图分类号: 

  • U461.6

图1

三自由度车辆模型"

图2

二自由度车辆模型"

图3

控制算法"

图4

RBF神经网络结构"

表1

单侧制动车轮选择逻辑"

δΔωr状态制动车轮
δ0Δωr>0不足fl,rl
Δωr<0过度fr,rr
δ<0Δωr>0过度fr,rl
Δωr<0不足fr,rr

表2

车辆参数"

车辆参数数值
整车质量/kg7690
质心到前轴距离/m3.012
质心到后轴距离/m1.478
前轮轮距/m2.030
后轮轮距/m1.863
质心到地面高度/m1.083

图5

90 km/h时的估计车辆状态参数"

图6

90 km/h的横摆角速度对比"

图7

90 km/h的质心侧偏角相平面图对比"

图8

108 km/h时的估计车辆状态参数"

图9

108 km/h的横摆角速度对比"

图10

108 km/h的质心侧偏角相平面图对比"

表3

ADCKF和CKF平均耗时"

仿真工况调用次数/次平均每次调用耗时/s
双移线工况1ADCKF12 6290.069 34
CKF12 6290.068 90
双移线工况2ADCKF12 5270.693 80
CKF12 5270.674 50
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