基于交互式多模型卡尔曼滤波的主动悬架控制

doi:10.19562/j.chinasae.qcgc.2023.07.011

Abstract

Abstract:

For the problem that it is difficult for fixed state observer to ensure the accuracy of road adaptive suspension state observation， the suspension state observer and controller is established on the basis of interactive multi-model Kalman filter （IMMKF）. Firstly， the road adaptive active suspension system is established based on the LQG algorithm and fuzzy control algorithm. Combined with harmonic superposition method， the A-B-D-C grade spatial domain road roughness model is generated as the input of the simulation system. Secondly， three kinds of IMMKF suspension state observer and controller are established taking the optimal LQG model of all grades of road as the sub-models. The simulation comparison shows that the observation accuracy of the 14-model IMMKF suspension state observer can be improved by 98.17% maximumly compared with the ordinary Kalman filter， and can be used to identify road grade， and the body acceleration of the adaptive active suspension controller based on the 14-model IMMKF is reduced by 75.99% compared with the passive suspension and 47.16% compared with the ordinary LQG active suspension， which verifies the superiority of the model.

Key words: interacting multiple model Kalman filter, fuzzy control, state observation, road grade recognition

Xiao Wu, Wenku Shi, Zhiyong Chen. Active Suspension Control Based on Interacting Multiple Model Kalman Filter[J].Automotive Engineering, 2023, 45(7): 1200-1211.

Figures/Tables 33

路面等级	A	B	C	D
$G q (n 0)$	1	64	256	1 024
$q 1 / 108$	1.543 8	1.476 5	1.361 6	1.264 6
$q 2$	678	805	957	1 103
$q 3$	0.000 1

项目	A	B	C	D
$z ¨ b (R M S)$	0.307 9	0.631 0	1.060 7	1.692 9
$z b - z t (R M S)$	0.003 3	0.008 5	0.019 0	0.039 2
$z b - z t (M A X)$	0.011 2	0.022 3	0.058 0	0.120 9
$z b - z r (R M S)$	0.000 7	0.001 7	0.003 3	0.006 2

观测量	模型	A	B	D	C
$z b - z t$	IMMKF14	10.25	11.98	18.61	16.63
	IMMKF7	22.71	14.79	18.34	16.71
	IMMKF4	48.51	24.43	21.15	19.73
	KF A	63.79	36.24	38.06	37.98
	KF C	70.17	46.06	25.33	27.62
$z ˙ b$	IMMKF14	3.40	1.69	0.32	0.58
	IMMKF7	10.40	7.15	2.33	3.89
	IMMKF4	21.56	13.30	5.00	7.98
	KF A	29.60	23.56	17.52	19.44
	KF C	34.65	20.90	6.82	12.32
$z t - z r$	IMMKF14	15.91	17.38	16.01	12.65
	IMMKF7	18.86	18.03	16.75	13.27
	IMMKF4	30.29	21.78	17.81	13.99
	KF A	41.48	32.61	35.49	31.56
	KF C	61.41	31.91	21.34	18.15
$z ˙ t$	IMMKF14	4.09	1.93	3.19	2.87
	IMMKF7	22.63	7.13	4.12	4.93
	IMMKF4	66.79	16.54	6.81	13.14
	KF A	98.71	32.34	18.20	28.75
	KF C	70.99	34.50	11.61	26.33

观测量	模型	A	B	C
$z ˙ b$	IMMKF14	3.40	1.69	0.58
	AUKF	5.00	7.00	9.00
	UKF	13.00	16.00	20.00
$z ˙ t$	IMMKF14	4.09	1.93	2.87
	AUKF	6.00	10.00	10.00
	UKF	15.00	18.00	22.22

输出量	模型	A	B	D	C
$z ¨ b / (m · s - 2)$	被动	0.733 9	1.484 8	5.892 7	2.788 8
	主动	0.320 4	0.652 7	2.677 2	1.247 5
	IMMKF	0.286 2	0.483 5	1.414 7	0.800 3
$z b - z t / m$	被动	0.012 1	0.024 6	0.097 8	0.047 0
	主动	0.003 4	0.006 8	0.027 5	0.013 4
	IMMKF	0.003 3	0.007 4	0.038 0	0.016 5
$z t - z r / m$	被动	0.000 4	0.000 8	0.003 2	0.001 7
	主动	0.000 8	0.001 6	0.006 4	0.003 0
	IMMKF	0.000 7	0.001 4	0.005 9	0.002 8

编号

G q (n 0) / × 10 - 6 m 3

q 1 × 108

q 2

Q R V

$A 0$

$A 1$

$A$

$A 2$

$A a b$

$B 1$

$B$

$B 2$

$B b c$

$C 1$

$C$

$C 2$

$C c d$

$D 1$

$D$

$D 2$

$E 1$

$2 2.67$

$2 3.33$

$24$

$2 4.67$

$25$

$2 5.33$

$26$

$2 6.67$

$27$

$2 7.33$

$28$

$2 8.67$

$29$

$2 9.33$

$210$

$2 10.67$

$2 11.33$

1.600 7

1.582 2

1.556 6

1.525 2

1.516 3

1.496 4

1.457 5

1.435 1

1.413 1

1.393 9

1.344 7

1.314 2

1.302 9

1.286 9

1.243 8

1.217 9

1.174 1

597

625

664

711

724

754

812

846

879

908

981

1 027

1 044

1 068

1 133

1 172

1 237

0.041 8

0.055 2

0.069 8

0.083 1

0.094 4

0.105 1

0.128 4

0.169 8

0.194 6

0.216 5

0.275 7

0.345 2

0.382 7

0.432 9

0.570 8

0.678 7

0.862 4

References 25

1	BAGHERI A， REZAEE M， HASHEMI S M. Genetic algorithm based optimization of model reference adaptive control approach for a vehicle active suspension system［C］. 3rd Industrial Simulation Conference 2005， Fraunhofer-IPK， Berlin， Germany， Jun 09-11. Fraunhofer-IPK， Berlin， Germany， 2005： 211-215.
2	PANG H， ZHANG X， YANG J J， et al. Adaptive backstepping-based control design for uncertain nonlinear active suspension system with input delay［J］. International Journal of Robust and Nonlinear Control， 2019， 29 （16）： 5781-5800.
3	ZHENG X Y， ZHANG H， YAN H C， et al. Active full-vehicle suspension control via cloud-aided adaptive backstepping approach［J］. IEEE Transactions on Cybernetics， 2020， 50 （7）： 3113-3124.
4	YI K S， SONG B S. Observer design for semi-active suspension control［J］. Vehicle System Dynamics， 1999， 32 （2-3）： 129-148.
5	DU M M， ZHAO D X， YANG M K， et al. Nonlinear extended state observer-based output feedback stabilization control for uncertain nonlinear half-car active suspension systems［J］. Nonlinear Dynamics， 2020， 100 （3）： 2483-2503.
6	WANG T P， CHEN S Z， REN H B， et al. State estimation and damping control for unmanned ground vehicles with semi-active suspension system［J］. Proceedings of the Institution of Mechanical Engineers Part D： Journal of Automobile Engineering， 2020， 234 （5）： 1361-1376.
7	WANG K Y， HE R， LI H， et al. Observer-based control for active suspension system with time-varying delay and uncertainty［J］. Advances in Mechanical Engineering， 2019， 11 （11）.
8	YI K S， SONG B S. Observer design for semi-active suspension control［J］. Vehicle System Dynamics， 1999， 32 （2-3）： 129-148.
9	WANG Z F， DONG M M， QIN Y C， et al. Suspension system state estimation using adaptive Kalman filtering based on road classification［J］. Vehicle System Dynamics， 2017， 55 （3）： 371-398.
10	WANG T C， LI Y M. Neural-network adaptive output-feedback saturation control for uncertain active suspension systems［J］. IEEE Transactions on Cybernetics， 2022， 52 （3）： 1881-1890.
11	WANG Z F， QIN Y C， GU L， et al. Vehicle system state estimation based on adaptive unscented Kalman filtering combing with road classification［J］. IEEE Access， 2017， 5： 27786-27799.
12	ZHANG Z P， XU N， CHEN H， et al. State observers for suspension systems with interacting multiple model unscented Kalman filter subject to markovian switching［J］. International Journal of Automotive Technology， 2021， 22 （6）： 1459-1473.
13	KAVIANIPOUR O， MONTAZERI-GH M， MOAZAMIZADEH M. Road profile measurement using the two degrees of freedom response-type mechanism［J］. Proceedings of the Institution of Mechanical Engineers Part C： Journal of Mechanical Engineering Science， 2015， 229 （6）： 1074-1087.
14	IMINE H， FRIDMAN L. Road profile estimation in heavy vehicle dynamics simulation［J］. International Journal of Vehicle Design， 2008， 47 （1-4）： 234-249.
15	MOTAMEDI M， TAHERI S， SANDU C， et al. Characterization of road profiles based on fractal properties and contact mechanics［J］. Rubber Chemistry and Technology， 2017， 90 （2）： 405-427.
16	YOUSEFZADEH M， AZADI S， SOLTANI A. Road profile estimation using neural network algorithm［J］. Journal of Mechanical Science and Technology， 2010， 24 （3）： 743-754.
17	ZHANG D J， XU X， LIN H， et al. Automatic road-marking detection and measurement from laser-scanning 3D profile data［J］. Automation in Construction， 2019， 108.
18	LEE J H， LEE S H， KANG D K， et al. Development of a 3D road profile measuring system for unpaved road severity analysis［J］. International Journal of Precision Engineering and Manufacturing， 2017， 18 （2）： 155-162.
19	QIN Y C， LANGARI R， GU L. The use of vehicle dynamic response to estimate road profile input in time domain［C］. ASME 7th Annual Dynamic Systems and Control Conference， San Antonio， TX， Oct 22-24. San Antonio， TX， 2014.
20	QIN Y C， DONG M M， LANGARI R， et al. Adaptive hybrid control of vehicle semiactive suspension based on road profile estimation［J］. Shock and Vibration， 2015， 2015.
21	CHEN S， XUE J J. Road roughness level identification based on BiGRU network［J］. IEEE Access， 2022， 10： 32696-32705.
22	LIU Y J， CUI D W. Research on road roughness based on NARX neural network［J］. Mathematical Problems in Engineering， 2021， 2021.
23	ZHANG Q X， HOU J L， HU X Y， et al. Vehicle parameter identification and road roughness estimation using vehicle responses measured in field tests［J］. Measurement， 2022， 199.
24	BOTSHEKAN M， ASAADI E， ROXON J， et al. Smartphone-enabled road condition monitoring： from accelerations to road roughness and excess energy dissipation［J］. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences， 2021， 477 （2246）.
25	HUANG W J， SU N C. A study of generalized normal distributions［J］. Communications in Statistics-Theory and Methods， 2017， 46 （11）：5612-5632.

[1]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[2]	CUI An, LI Bin, WANG Xue-Liang, ZHANG Shi-Zhan. [J]. , 2016, 38(12): 1521 -1525 .
[3]	YU Zhuo-Ping, HAN Wei, XIONG Lu. [J]. , 2017, 39(1): 52 -60 .
[4]	CUI Jun-Jia, YUAN Wei, LI Guang-Yao. [J]. , 2017, 39(1): 113 -120 .
[5]	RONG Bing, XIAO Pan, ZHOU Jian-Wen, LIU Yi-Lu. [J]. , 2017, 39(2): 214 -219 .
[6]	ZHANG Zhong-Shi, WANG Li-Fang, ZHANG Jun-Zhi. [J]. , 2017, 39(3): 288 -295 .
[7]	CHEN Jun-Hao, REN Jin-Dong, LIU Tao. [J]. , 2017, 39(3): 351 -356 .
[8]	JIN Geng-Ri, GUAN Xin, ZHAN Jun, GUO Rui. [J]. , 2017, 39(3): 364 -368 .
[9]	CHEN Jun, WANG Yong, SHI Feng, ZHOU Long, FENG Wei. [J]. , 2017, 39(4): 412 -417 .
[10]	LI Ling, SHI Shu-Ming, WANG Xian-Bin, ZHANG Xiang-Dong. [J]. , 2017, 39(4): 440 -446 .

采样步数	模型	A	B	C	D
1	IMMKF14	81.00	83.00	95.00	70.50
	IMMKF7	60.50	42.00	77.00	35.50
	IMMKF4	84.50	83.00	96.50	67.50
10	IMMKF14	95.00	90.00	100.00	90.00
	IMMKF7	65.00	55.00	90.00	45.00
	IMMKF4	90.00	85.00	100.00	70.00
20	IMMKF14	100.00	100.00	100.00	90.00
	IMMKF7	60.00	50.00	100.00	60.00
	IMMKF4	100.00	90.00	100.00	80.00

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Active Suspension Control Based on Interacting Multiple Model Kalman Filter

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