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

Abstract

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

CLC Number:

U461.6

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.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

One-sided brake wheels selection logic"

$δ$	$Δ ω r$	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	$f r, r r$

Table 1

Table 2

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Table 3

References 16

1	Antonov S, Fehn A, Kugi A. Unscented Kalman filter for vehicle state estimation[J]. Vehicle System Dynamics, 2011, 49(9): 1497-1520.
2	Reif K, Renner K, Saeger M. Using the unscented Kalman filter and a non-linear two-track model for vehicle state estimation[J]. Ifac Proceedings Volumes, 2008, 41(2): 8570-8575.
3	宋传学, 肖峰, 刘思含, 等. 基于无迹卡尔曼滤波的轮毂电机驱动车辆状态观测[J]. 吉林大学学报: 工学版, 2016, 46(2): 333-339.
	Song Chuan-xue, Xiao Feng, Liu Si-han, et al. State estimation of electric vehicle with in-wheel motors based on UKF[J]. Journal of Jilin University(Engineering and Technology Edition), 2016, 46(2): 333-339.
4	田彦涛, 张宇, 王晓玉, 等. 基于平方根无迹卡尔曼滤波算法的电动汽车质心侧偏角估计[J]. 吉林大学学报：工学版, 2018, 48(5): 845-852.
	Tian Yan-tao, Zhang Yu, Wang Xiao-yu, et al. Estimation of side-slip angle of electric vehicle based on square-root unscented Kalman fliter algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 845-852.
5	张家旭, 李静. 采用自适应无迹卡尔曼滤波器的车速和路面附着系数估计[J]. 西安交通大学学报, 2016, 50(3): 68-75.
	Zhang Jia-xu, Li Jing. Estimation of vehicle speed and tire-road adhesion coefficient by adaptive unscented Kalman filter[J]. Journal of Xi’an Jiaotong University, 2016, 50(3): 68-75.
6	金贤建, 殷国栋, 陈南, 等. 分布式驱动电动汽车的平方根容积卡尔曼滤波状态观测[J]. 东南大学学报: 自然科学版, 2016, 46(5): 992-996.
	Jin Xian-jian, Yin Guo-dong, Chen Nan, et al. State observation of distributed drive electric vehicle using square root cubature Kalman filter[J]. Journal of Southeast University(Natural Science Edition), 2016, 46(5): 992-996.
7	李静, 石求军, 刘鹏, 等. 基于纵向车速估算的商用车ABS神经网络滑模控制[J]. 吉林大学学报：工学版, 2019, 49(4): 1017-1025.
	Li Jing, Shi Qiu-jun, Liu Peng, et al. Neural network sliding mode control of commercial vehicle ABS based on longitudinal vehicle speed estimation[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1017-1025.
8	张召友, 郝燕玲, 吴旭. 3种确定性采样非线性滤波算法的复杂度分析[J]. 哈尔滨工业大学学报, 2013, 45(12): 111-115.
	Zhang Zhao-you, Hao Yan-ling, Wu Xu. Complexity analysis of three deterministic sampling nonlinear filtering algorithms[J]. Journal of Harbin Institute of Technology, 2013, 45(12): 111-115.
9	唐李军. Cubature卡尔曼滤波及其在导航中的应用研究[D]. 哈尔滨: 哈尔滨工程大学自动化学院, 2012.
	Tang Li-jun. Cubature Kalman filter and application research on navigation[D]. Harbin： College of Automation， Harbin Engineering University, 2012.
10	金辉, 李世杰. 基于极限车速的车辆稳定性控制研究[J]. 汽车工程, 2018, 40(1): 48-56.
	Jin Hui, Li Shi-jie. A research on vehicle stability control based on limited speed[J]. Automotive Engineering, 2018, 40(1): 48-56.
11	李寿涛, 马用学, 郭鹏程, 等. 一种变逻辑门限值的车辆稳定性控制策略研究[J]. 汽车工程, 2015, 37(7): 782-787.
	Li Shou-tao, Ma Yong-xue, Guo Peng-cheng, et al. A study on vehicle stability control strategy with variable threshold[J]. Automotive Engineering, 2015, 37(7): 782-787.
12	Chen Y H, Hedrick J K, Guo K H. A novel direct yaw moment controller for in-wheel motor electric vehicles[J]. Vehicle System Dynamics, 2013, 51(6): 925-942.
13	周磊, 张向文. 基于Dugoff轮胎模型的爆胎车辆运动学仿真[J]. 计算机仿真, 2012, 29(6): 308-311.
	Zhou Lei, Zhang Xiang-wen. Simulation of vehicle dynamics in tire blow-out process based on Dugoff tire model[J]. Computer Simulation, 2012, 29(6): 308-311.
14	张汉国, 张洪钺. 阻止自适应Kalman滤波发散的补救方法[J]. 控制与决策, 1991, 6(1): 53-56.
	Zhang Han-guo, Zhang Hong-yue. Remedy to prevent adaptive Kalman filter divergence[J]. Control and Decision, 1991, 6(1): 53-56.
15	Ding S H, Liu L, Zhang W X. Sliding mode direct yaw-moment control design for in-wheel electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2017, 64(8): 6752-6762.
16	刘金坤. 滑模变结构控制MATLAB仿真(第3版)基本理论与设计方法[M]. 北京: 清华大学出版社, 2015.

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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

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

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