基于车辆状态估计的商用车ESC神经网络滑模控制

doi:10.13229/j.cnki.jdxbgxb20190535

摘要/Abstract

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

李静,石求军,洪良,刘鹏. 基于车辆状态估计的商用车ESC神经网络滑模控制[J]. 吉林大学学报(工学版), 2020, 50(5): 1545-1555.

Jing LI,Qiu-jun SHI,Liang HONG,Peng LIU. Commercial vehicle ESC neural network sliding mode control based on vehicle state estimation[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1545-1555.

图/表 13

图1

图2

图3

图4

表1

单侧制动车轮选择逻辑"

$δ$	$Δ ω r$	状态	制动车轮
$δ ≥ 0$	$Δ ω r > 0$	不足	$f l, r l$
$δ ≥ 0$	$Δ ω r < 0$	过度	$f r, r r$
$δ < 0$	$Δ ω r > 0$	过度	$f r, r l$
$δ < 0$	$Δ ω r < 0$	不足	$f r, r r$

表1

表2

图5

图6

图7

图8

图9

图10

表3

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车辆参数	数值
整车质量/kg	7690
质心到前轴距离/m	3.012
质心到后轴距离/m	1.478
前轮轮距/m	2.030
后轮轮距/m	1.863
质心到地面高度/m	1.083

仿真工况		调用次数/次	平均每次调用耗时/s
双移线工况1	ADCKF	12 629	0.069 34
双移线工况1	CKF	12 629	0.068 90
双移线工况2	ADCKF	12 527	0.693 80
双移线工况2	CKF	12 527	0.674 50