基于路面垂向激励博弈决策的悬架模型切换预瞄控制研究

吴骁; 陈志勇; 陈龙; 刘巧斌; 杨红波; 史文库

doi:10.19818/j.cnki.1671-1637.2025.03.018

基于路面垂向激励博弈决策的悬架模型切换预瞄控制研究

doi: 10.19818/j.cnki.1671-1637.2025.03.018

1.
吉林大学汽车工程学院，吉林长春 130022
2.
太原理工大学机械与运载工程学院，山西太原 030024
3.
华南理工大学机械与汽车工程学院，广东广州 510641
4.
江苏大学汽车与交通工程学院，江苏镇江 212013

基金项目:

国家自然科学联合基金项目 U24A6008

中国博士后科学基金面上项目 2024M753542

汽车仿真与控制国家重点实验室开放课题 20230101

详细信息

作者简介:
吴骁(1998-)，男，陕西西安人，吉林大学工学博士研究生，从事车辆系统动力学与控制研究

通讯作者:
陈志勇(1980-)，男，吉林长春人，吉林大学教授，工学博士

中图分类号: U463.1
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- 被引次数: 0
出版历程
- 收稿日期: 2024-08-26
- 录用日期: 2025-04-30
- 修回日期: 2025-02-14
- 刊出日期: 2025-06-28

Research on suspension model switching preview control based on road surface vertical excitation game decision

1.
College of Automotive Engineering, Jilin University, Changchun 130022, Jilin, China
2.
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
3.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, Guangdong, China
4.
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

Funds:

Joint Fund of National Natural Science Foundation of China U24A6008

China Postdoctoral Science Foundation Project 2024M753542

Open Project of the State Key Laboratory of Automotive Simulation and Control 20230101

More Information

Corresponding author: CHEN Zhi-yong (1980-), male, professor, PhD, chen_zy@jlu.edu.cn

Article Text (Baidu Translation)

摘要

摘要: 针对表观路面不平度与实际路面垂向激励不同的情况，提出一种基于博弈论的路面垂向激励感知决策方法，用于悬架预瞄控制，提升了车辆在复杂路面激励工况下的平顺性；基于悬架最优控制理论，针对不同道路激励模式，采用多目标优化算法优化控制模型，综合所有激励模式下的控制模型，建立了悬架模型切换控制系统，根据路面垂向激励切换控制器的控制参数，使悬架的振动状态保持最优；对比了预瞄悬架在相同路面激励、不同控制器参数下的振动响应结果，分析了控制器参数对振动响应的影响；将悬架的控制视为控制器与路面激励的博弈，以提升平顺性为目标，在不同道路激励下，当道路预瞄法与状态观测法的结果相互矛盾时，基于博弈论分析出其中的最优结果，作为悬架控制模型切换的依据。研究结果表明：基于博弈论，控制系统应根据功率谱密度指数或振幅较大的路面切换控制模型以提升平顺性；相对于未采用博弈论的纯预瞄控制模型，连续路面上，当道路预瞄结果为A级，状态观测结果为D级时，基于博弈论的最优控制模型的车身加速度均方根值降低了14.24%；冲击路面上，车身加速度峰值降低了5.86%；对于混合路面，车身加速度均方根值降低了11.60%，各工况下悬架动挠度、轮胎动变形、能耗均方根值的提升均不超过10%。该方法可有效提升车辆在各种复杂路面工况下的平顺性。
- 汽车工程 /
- 路面识别 /
- 博弈论 /
- 预瞄控制 /
- 模型切换控制 /
- 状态观测 /
- 多目标优化
Abstract: To address the inconsistency between apparent road surface roughness and actual vertical road excitation, a game theory-based vertical road excitation perception and decision-making method was proposed for preview control of the suspension model, aiming to improve the ride comfort of vehicles under complex road excitation conditions. Based on optimal suspension control theory, a multi-objective optimization algorithm was used to optimize the control models for different road excitation patterns. By integrating the control models under all excitation patterns, a suspension model switching control system was established. The controller parameters were switched according to vertical road excitation so that the vibration state of the suspension was kept optimal. The vibration responses under the same road excitation and different controller parameters were compared, and the influence of controller parameters on vibration responses was analyzed. Suspension control was regarded as a game between the controller and the road excitation. To improve ride comfort, when the results of the road preview method and the state observation method were contradictory under different road excitations, the optimal result was analyzed based on game theory and used as the basis for suspension control model switching. Analysis results show that based on the game theory, the control system should switch the control model according to the power spectral density index or road amplitude to improve ride comfort. Compared with the pure preview control model without game theory, on continuous road surfaces, when the preview result is grade A, and the state observation result is grade D, the root mean square (RMS) of vehicle acceleration of the optimal control model based on the game theory decreases by 14.24%. On the impact road surfaces, the peak value of the vehicle acceleration decreases by 5.86%. On mixed road surfaces, the RMS of the vehicle acceleration decreases by 11.60%, and the RMS of suspension's dynamic deflection, tire's dynamic travel, and energy consumption all increase by less than 10%. This method is applicable to improving the ride comfort of vehicles equipped with preview suspension systems under various complex road conditions.
- automotive engineering /
- road surface recognition /
- game theory /
- preview control /
- model switching control /
- state observation /
- multi-objective optimization

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图 1 控制系统架构

Figure 1. Structure of control system

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图 2 四分之一车辆模型

Figure 2. Model of quarter car

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图 3 路面激励与车身加速度的仿真结果、多目标优化结果

Figure 3. Results of simulation and multi-objective optimization of road excitation and vehicle body acceleration

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图 4 冲击路面多目标优化结果

Figure 4. Multi-objective optimization results of impact road surface

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图 5 混合路面多目标优化结果

Figure 5. Multi-objective optimization results of mixed road surface

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图 6 各悬架控制器仿真结果对比

Figure 6. Comparison of simulation results of each suspension controller

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图 7 道路不平度对比

Figure 7. Comparison of road roughnesses

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图 8 连续路面激励情况1仿真结果

Figure 8. Simulation results of continuous road surface excitation case 1

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图 9 连续路面激励情况2仿真结果

Figure 9. Simulation results of continuous road surface excitation case 2

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图 10 混合路面激励情况1仿真结果

Figure 10. Simulation results of mixed road surface excitation case 1

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图 11 混合路面激励情况2仿真结果

Figure 11. Simulation results of mixed road surface excitation case 2

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图 12 悬架试验台架

Figure 12. Suspension experiment platform

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图 13 工况1试验结果对比

Figure 13. Comparison of suspension experiment results in case 1

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图 14 工况2试验结果对比

Figure 14. Comparison of suspension experiment results in case 2

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图 15 工况3试验结果对比

Figure 15. Comparison of suspension experiment results in case 3

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表 1 不同连续路面激励及控制参数下的振动结果对比

Table 1. Comparison of vibration results of different continuous road surface excitation and controller parameters

振动结果	激励等级	控制模型对应的路面等级
振动结果	激励等级	ISO A d=4	ISO B d=6	ISO C d=8	ISO D d=10
车身加速度均方根值/(m·s^-2)	ISO A	0.287 8	0.257 0	0.245 7	0.244 6
	ISO B	0.557 1	0.517 5	0.495 3	0.471 6
	ISO C	1.096 5	1.030 9	1.016 5	1.009 3
	ISO D	2.234 8	2.026 9	1.950 5	1.943 3
悬架动挠度均方根值/ m	ISO A	0.004 4	0.004 5	0.004 5	0.004 6
	ISO B	0.013 2	0.014 4	0.015 2	0.016 6
	ISO C	0.026 9	0.027 6	0.028 9	0.030 8
	ISO D	0.060 5	0.061 3	0.061 8	0.063 3
理论能耗均方根值/W	ISO A	3.173 6	3.272 1	3.324 0	3.395 8
	ISO B	17.387 8	18.082 7	18.753 1	18.991 4
	ISO C	100.528 0	108.322 7	119.051 6	152.293 1
	ISO D	370.536 1	383.236 9	399.923 5	409.178 6

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表 2 车路标准式博弈策略集及收益

Table 2. Strategy sets and benefits of vehicle-road game

策略组合		车O₁
		策略集		收益
		4	8	4	8
路O₂	4	{4, 4}	{8, 4}	{0, 0}	{1, -1}
	6	{4, 6}	{8, 6}	{-1, 1}	{1, -1}
	8	{4, 8}	{8, 8}	{-1, 1}	{0, 0}
	10	{4, 10}	{8, 10}	{-1, 1}	{-1, 1}

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表 3 不同离散路面激励及控制模型下的振动结果对比

Table 3. Comparison of vibration results of different hump road excitation and control models

参数	路面激励振幅/m	控制模型对应的路面振幅/m
参数	路面激励振幅/m	0.01	0.05	0.10
车身加速度均方根值/ (m·s^-2)	0.01	0.059 9	0.058 1	0.056 2
	0.05	0.299 4	0.290 5	0.280 9
	0.10	0.598 8	0.581 1	0.561 4
悬架动挠度均方根值/m	0.01	1.063 6	1.066 4	1.070 1
	0.05	5.318 2	5.331 8	5.349 8
	0.10	10.636 5	10.663 5	10.699 7
理论能耗均方根值/ W	0.01	0.140 2	0.154 0	0.167 3
	0.05	3.504 6	3.848 9	4.181 8
	0.10	14.018 4	15.395 6	16.727 0

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表 4 模型性能对比

Table 4. Comparison of model performance %

工况	模型	性能参数
工况	模型	车身加速度	悬架动挠度	轮胎动载荷	理论能耗
连续工况情况1	对照LQR	-14.24	4.03	4.97	1.52
连续工况情况2	对照LQR	-14.23	4.26	5.08	0.62
混合工况情况1	对照LQR	-11.60	5.33	5.42	1.32
混合工况情况1	基于观测	-4.07	0.67	3.01	1.34
混合工况情况2	对照LQR	-10.05	5.24	5.09	0.64
混合工况情况2	基于观测	-10.85	5.31	5.43	0.72

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