基于扰动观测器的主动悬架切换控制算法研究

doi:10.19562/j.chinasae.qcgc.2024.10.003

Abstract

Abstract:

Suspension control requires good balance between ride comfort and driving stability， while considering system uncertainties， which is a complex task. In this paper， a disturbance observer-based suboptimal-nonsingular terminal sliding mode switching control algorithm （DOB-SNTSM） is proposed， with considerations of suspension dynamic performance indicators， algorithm robustness， and cost factors. Firstly， using spring mass acceleration information as input and by Kalman filter design， effective estimation of suspension deflection and spring mass velocity is achieved. Subsequently， a disturbance observer is devised to estimate uncertainties within the suspension system， with the disturbance estimation serving as feedforward compensation. Next， based on the sliding mode surface function， a suboptimal-nonsingular terminal sliding mode switching control algorithm is proposed， integrating with the feedforward compensation from the disturbance observer to formulate a novel active suspension control strategy. Finally， simulation and bench tests are conducted on both convex road surfaces and smooth random road surfaces. The results show that the introduction of disturbance observers can significantly improve the ride comfort index of the suspension. Compared to the SNTSM algorithm with the classical sky-hook control， the ideal state LQR method and without disturbance observer， the new algorithm not only effectively balances various suspension performance indicators but also achieves control effect close to the ideal state LQR using solely spring mass acceleration information. Additionally， the controller switching scheme significantly enhances algorithm robustness.

Key words: active suspension, disturbance observer, switching control, suboptimal control, nonsingular terminal sliding mode control, Kalman filter

Xiaokai Chen,Feng Chen,Xiang Liu,Hongyu Liu,Xiaoyu Wang. Research on DOB-Based Switching Control Algorithm for Active Suspension System[J].Automotive Engineering, 2024, 46(10): 1744-1754.

Figures/Tables 15

参数名称	符号/单位	数值
簧载质量	$m s / k g$	2.45
非簧载质量	$m u / k g$	1
悬架刚度	$k s / (N · m - 1)$	900
悬架阻尼	$c s / (N · s · m - 1)$	7.5
轮胎刚度	$k u / (N · m - 1)$	2 500
仿真车速	$v c / (m · s - 1)$	3
凸包路面高度	$H / m$	0.02
凸包路面长度	$L / m$	1.5
参考空间频率	$n 0 / m - 1$	0.1
空间下截止频率	$n m i n / m - 1$	0.011
路面不平度系数	$G x r n 0 / m 3$	0.000 256

参数	数值
理想天棚阻尼 $/ (N · s · m - 1)$	50
DOB观测增益	15
LQR算法 $Q$ 矩阵	［450，50，5，1］
NTSM参数 $p$	5
NTSM参数 $q$	3
NTSM参数 $β$	2

控制方法	簧载质量加速度/ $(m · s - 2)$	轮胎动变形/ $(10 - 3 m)$
被动悬架	$1.877$	$2.10$
天棚控制	$0.550$	$0.660$
理想状态LQR	$0.353 (35.8 %)$	$0.452 (31.5 %)$
SNTSM	$0.457 (16.9 %)$	$0.514 (22.1 %)$
DOB-SNTSM	$0.417 (24.2 %)$	$0.507 (23.2 %)$

控制方法	簧载质量加速度/ $(m · s - 2)$	轮胎动变形/ $(10 - 3 m)$
被动悬架	$1.896$	$2.56$
天棚控制	$0.952$	$1.74$
理想状态LQR	$0.776 (18.5 %)$	$1.91 (- 9.8 %)$
SNTSM	$0.893 (6.1 %)$	$1.93 (- 10.9 %)$
DOB-SNTSM	$0.755 (20.7 %)$	$2.06 (- 18.4 %)$

控制方法	簧载质量加速度/ $(m · s - 2)$	轮胎动变形/ $(10 - 3 m)$
被动悬架	$1.301$	$1.54$
天棚控制	$0.541$	$0.922$
理想状态LQR	$0.446 (17.6 %)$	$0.879 (4.7 %)$
SNTSM	$0.522 (3.5 %)$	$0.905 (1.8 %)$
DOB-SNTSM	$0.489 (9.6 %)$	$0.913 (1.0 %)$

控制方法	簧载质量加速度/ $(m · s - 2)$	轮胎动变形/ $(10 - 3 m)$
被动悬架	$0.275$	$1.15$
天棚控制	$0.201$	$1.15$
理想状态LQR	$0.164 (18.4 %)$	$1.14 (0.9 %)$
SNTSM	$0.198 (1.5 %)$	$1.12 (2.6 %)$
DOB-SNTSM	$0.183 (9.0 %)$	$1.19 (- 3.5 %)$

References 15

1	DONG X， FEI Z， ZHANG Z， et al. Gray skyhook predictive control of magnetorheological semi-active seat suspension with time delay［J］. Smart Materials and Structures， 2023， 32（11）： 115010.
2	刘雁玲，颜龙，杨晓峰，等. 基于ADD正实网络的车辆ISD悬架优化设计与性能研究［J］. 振动与冲击， 2021， 40（11）： 262-268.
	LIU Y L， YAN L， YANG X F， et al. Research on optimal design and performance of vehicle ISD suspension based on ADD positive real network ［J］. Journal of Vibration and Shock， 2021， 40（11）： 262-268.
3	WANG G， KOU F， ZHANG X， et al. A new energy-feeding variable damping control strategy for electromagnetic hybrid suspension systems［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2023： 09544070231205356.
4	郭孔辉，王杨. 一种改进的加速度阻尼半主动控制策略研究［J］. 汽车工程， 2019， 41（5）： 481-486.
	GUO K H， WANG Y. Research on an improved acceleration damping semi-active control strategy ［J］. Automotive Engineering， 2019， 41（5）： 481-486.
5	陈潇凯，曾洺锴，刘向，等. 基于VSL-MPC的半主动悬架预瞄控制研究［J］. 汽车工程， 2022， 44（10）： 1537-1546.
	CHEN X K， ZENG M K， LIU X， et al. Research on pre-sight control of semi-active suspension based on VSL-MPC ［J］. Automotive Engineering， 2022， 44（10）： 1537-1546.
6	YUAN C Y， LI K T， ZANG G R， et al. LQR Optimization of semi-active suspension LQR parameters based on local optimization with a skipping out particle swarm algorithm［C］. International Conference on Mechanical Design and Simulation （MDS 2022）： Volume 12261. SPIE， 2022： 1111-1116.
7	李子先，潘世举，朱愿，等. 基于状态反馈和预瞄前馈的智能车半主动悬架控制［J］. 汽车工程， 2023， 45（5）： 735-745.
	LI Z X， PAN S J， ZHU Y， et al. Semi-active suspension control of intelligent vehicle based on state feedback and preview feedforward ［J］. Automotive Engineering， 2023， 45（5）： 735-745.
8	YU X， LIU X， WANG X， et al. Vibration control of improved LQG for wheel drive electric vehicle based on uncertain parameters［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2021，235（8）：2253-2264.
9	吴骁，史文库，陈志勇. 基于交互式多模型卡尔曼滤波的主动悬架控制［J］. 汽车工程， 2023， 45（7）： 1200-1211，1253.
	WU X， SHI W K， CHEN Z Y. Active suspension control based on interactive multi-model Kalman filter ［J］. Automotive Engineering， 2023， 45（7）： 1200-1211，1253.
10	于曰伟，赵雷雷，周长城，等. 车辆座椅次优控制主动悬架设计及分析［J］. 北京邮电大学学报， 2021， 44（3）： 87-93.
	YU Y W， ZHAO L L， ZHOU C C， et al. Design and analysis of vehicle seat suboptimal control active suspension ［J］. Journal of Beijing University of Posts and Telecommunications， 2021， 44（3）： 87-93.
11	鲁红伟，张志飞，徐中明，等. 汽车主动悬架次优控制策略等效性研究［J］. 振动与冲击， 2023， 42（23）： 316-324.
	LU H W， ZHANG Z F， XU Z M， et al. Research on the equivalence of suboptimal control strategy for automotive active suspension ［J］. Journal of Vibration and Shock， 2023， 42（23）： 316-324.
12	SOOSAIRAJ A S， KANDAVEL A. Ride comfort analysis of driver seat using super twisting sliding mode controlled magnetorheological suspension system［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2021， 235（14）： 3606-3618.
13	CHEN H， LIU Y J， LIU L， et al. Anti-saturation-based adaptive sliding-mode control for active suspension systems with time-varying vertical displacement and speed constraints［J］. IEEE Transactions on Cybernetics， 2022， 52（7）： 6244-6254.
14	HO C M， TRAN D T， AHN K K. Adaptive sliding mode control based nonlinear disturbance observer for active suspension with pneumatic spring［J］. Journal of Sound and Vibration， 2021， 509： 116241.
15	汪若尘，王英杰，丁仁凯，等. 基于卡尔曼观测器的磁流变悬架半主动控制［J］. 华中科技大学学报（自然科学版）， 2020， 48（12）： 49-54.
	WANG R C， WANG Y J， DING R K， et al. Semi-active control of magnetorheological suspension based on Kalman observer ［J］. Journal of Huazhong University of Science and Technology （Natural Science Edition）， 2020， 48（12）： 49-54.

[1]	ZHOU Wei, ZHANG Cheng-Ning, LI Jun-Qiu. [J]. , 2016, 38(12): 1407 -1414 .
[2]	LI Huan, HUANG Ying, ZHANG Fu-Jun, ZHAO Yu, GE Yan-Wu. [J]. , 2016, 38(12): 1420 -1426 .
[3]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[4]	ZONG Yi-Qi, GU Zheng-Qi, LUO Ze-Min, JIANG Cai-Mao, ZHANG Qi-Dong, YANG Zhen-Dong. [J]. , 2016, 38(12): 1427 -1433 .
[5]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[6]	SHEN Zhe, WANG Yi-Gang, YANG Zhi-Gang, LI Fang-Xu. [J]. , 2016, 38(12): 1440 -1445 .
[7]	ZHANG Yi-Bing, 吕Jian-Jun , YUAN Guang-Hui. [J]. , 2016, 38(12): 1467 -1470 .
[8]	KONG Xiang-Dong, ZHANG Zhen-Dong, XIE Nai-Liu, CHENG Qiang, YIN Cong-Bo. [J]. , 2016, 38(12): 1471 -1476 .
[9]	ZHANG Lei, ZHANG Jin-Qiu, LUO Tao, BI Zhan-Dong, YAO Jun. [J]. , 2016, 38(12): 1494 -1499 .
[10]	PENG Qian-Lei, GUI Liang-Jin, FAN Zi-Jie. [J]. , 2016, 38(12): 1500 -1507 .

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Research on DOB-Based Switching Control Algorithm for Active Suspension System

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