电磁阀半主动悬架线性变参数μ综合鲁棒控制

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摘要针对电磁阀半主动悬架系统中的非线性和不确定性问题以及多个性能目标之间的矛盾，设计了一种线性变参数(Linear Parameter Varying, LPV) μ综合鲁棒控制器。建立电磁阀非线性阻尼力力学模型并开展台架试验验证模型的准确性，构建了七自由度悬架系统的非线性LPV模型；对悬架参数不确定性和系统建模误差进行分析，并考虑传感器测量噪声的影响，设计了一种基于非线性和混合不确定性的LPV-μ综合鲁棒控制器；最后进行整车电磁阀悬架系统仿真和实车试验。仿真结果表明：在随机扰动下该控制器使车身垂向加速度均方根值降低46.47％，俯仰角加速度降低46.47％，侧倾角加速度降低50.68％，具有良好的抗干扰能力和可控性。试验结果表明：LPV-μ综合控制下车身垂向加速度、俯仰角加速度和侧倾角加速度均方根值分别降低36.6％、30.14％和39.47％，并且保证悬架动挠度小于0.06 m，验证了该控制策略的有效性。

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	寇发荣1
	李盛霖2
	杨旭东2
	邢龙龙2

关键词 ：电磁阀半主动悬架, 非线性和不确定性, 线性变参数系统, 多目标控制, LPV-μ综合

Abstract：A linear parameter varying(LPV) μ synthesis robust controller is designed for the nonlinearity, uncertainty problem, and the contradiction between multiple performance objectives in solenoid valve semi-active suspension systems. A nonlinear damping force dynamics model of the solenoid valve was established and performed on a bench test to verify the accuracy of the model. A nonlinear LPV model for a seven-degree-of-freedom suspension system was constructed. An LPV-μ synthesis robust controller based on nonlinear and mixed uncertainties is designed with analysis of suspension parameter uncertainties and system modeling errors and consideration of the effects of sensor measurement noise. Finally, the full vehicle solenoid suspension system simulation and real vehicle test were conducted. Simulation results show that the controller reduces the root mean square value of body vertical acceleration for 46.47%, the pitch angle acceleration for 46.47%, and the roll angle acceleration for 50.68% under random disturbance. It has excellent anti-jamming ability and controllability. The test results show that the root mean square values of the vertical acceleration, pitch angle acceleration, and roll angle acceleration are reduced by 36.6%, 30.14%, and 39.47%, respectively, under the LPV-μ synthesis control. In addition, the dynamic deflection of the suspension is ensured to be less than 0.06 m, which verifies the effectiveness of the control strategy.

Key words： Solenoid semi-active suspension nonlinearity and uncertainty linear parameter varying systems multi-objective control LPV-μ synthesis

收稿日期: 2023-05-22 出版日期: 2024-04-28

引用本文:

寇发荣1，李盛霖2，杨旭东2，邢龙龙2. 电磁阀半主动悬架线性变参数μ综合鲁棒控制[J]. 振动与冲击, 2024, 43(8): 221-231.
KOU Farong1,LI Shenglin2,YANG Xudong2,XING Longlong2. Linear variable parameter μ synthesis robust control of solenoid valve semi-active suspension. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(8): 221-231.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I8/221

[1] Abdul Zahra A K, Abdalla T Y. Design of fuzzy super twisting sliding mode control scheme for unknown full vehicle active suspension systems using an artificial bee colony optimization algorithm[J]. Asian Journal of Control, 2021, 23(4): 1966-1981. [2] 朱茂桃, 唐伟, 王道勇, 等. 半主动液压减振器动态特性建模与试验研究[J]. 振动与冲击, 2018, 37(07): 139-145. ZHU Mao-tao, TANG Wei, WANG Dao-yong, et al. Modeling and experimental study of dynamic characteristics of semi-active hydraulic dampers[J]. Vibration and Shock, 2018, 37(07): 139-145. [3] Taghavifar H, Rakheja S. Multi-objective optimal robust seat suspension control of off-road vehicles in the presence of disturbance and parametric uncertainty using metaheuristics[J]. IEEE Transactions on Intelligent Vehicles, 2019, 5(3): 372-384. [4] Wang H, Wong P K, Zhao J, et al. Observer-based robust gain-scheduled control for semi-active air suspension systems subject to uncertainties and external disturbance[J]. Mechanical Systems and Signal Processing, 2022, 173: 109045. [5] Soliman A M A, Kaldas M M S. Semi-active suspension systems from research to mass-market–A review[J]. Journal of Low Frequency Noise, Vibration and Active Control, 2021, 40(2): 1005-1023. [6] Moghadam A R, Kebriaei H. Stochastic sliding mode control of active vehicle suspension with mismatched uncertainty and multiplicative perturbations[J]. Asian Journal of Control, 2020, 22(6): 2330-2339. [7] Savaia G, Sohn Y, Formentin S, et al. Experimental automatic calibration of a semi-active suspension controller via Bayesian optimization[J]. Control Engineering Practice, 2021, 112: 104826. [8] Kararsiz G, Paksoy M, Metin M, et al. An adaptive control approach for semi-active suspension systems under unknown road disturbance input using hardware-in-the-loop simulation[J]. Transactions of the Institute of Measurement and Control, 2021, 43(5): 995-1008. [9] 夏长高,梁艾金,杨宏图,等.内置电磁阀式阻尼连续可调减振器设计与试验[J].农业机械学报,2018,49(05):397-403. XIA Chang-gao, LIANG Ai-jin, YANG Hong-tu, et al. Design and test of internal solenoid valve type Continuous Damping Control damper[J]. Journal of Agricultural Machinery,2018,49(05):397-403. [10] 赵凯旋, 陈双. 汽车半主动悬架系统阻尼可调减振器AMESim模型参数辨识[J]. 科学技术与工程, 2018, 18(36): 253-260. ZHAO Kai-xuan, CHEN Shuang. Parameter identification of damping adjustable damper AMESim model for automotive semi-active suspension system [J]. Science, Technology and Engineering, 2018, 18(36): 253-260. [11] 陈双, 赵凯旋. 电磁阀式阻尼可调减振器AMESim建模研究[J]. 机械设计与制造, 2019(04): 31-38. CHEN Shuang, ZHAO Kai-xuan. Solenoid valve type damping adjustable damper AMESim modeling study[J]. Mechanical Design and Manufacture, 2019(04): 31-38. [12] 寇发荣, 王睿, 洪锋, 等. 内置电磁阀式半主动悬架自适应反演控制[J].振动与冲击, 2022, 41(01): 1-9. KOU Fa-rong, WANG Rui, HONG Feng, et al. Adaptive inverse control of semi-active suspension with built-in solenoid valve[J]. Vibration and Shock, 2022, 41(01): 1-9. [13] 张亮修, 王宇, 吴光强, 等. 汽车阻尼可调半主动悬架混杂模型预测控制[J]. 西安交通大学学报, 2017, 51(11): 156-164. ZHANG Liang-xiu, WANG Yu, WU Guang-qiang, et al. Predictive control of damping adjustable semi-active suspension with mixed models for automobiles[J]. Journal of Xi'an Jiaotong University, 2017, 51(11): 156-164. [14] 陈龙, 马瑞, 王寿静, 等. 车辆半主动悬架阻尼多模式切换控制研究[J]. 振动与冲击, 2020, 39(13): 148-155. CHEN Long, MA Rui, WANG Shou-jing, et al. Research on multi-mode switching control of vehicle semi-active suspension damping [J]. Vibration and Shock, 2020, 39(13): 148-155. [15] Jeyasenthil R, Yoon D S, Choi S B, et al. Robust semiactive control of a half‐car vehicle suspension system with magnetorheological dampers: Quantitative feedback theory approach with dynamic decoupler[J]. International Journal of Robust and Nonlinear Control, 2021, 31(4): 1418-1435. [16] Abdul Aziz M, Mohtasim S M, Ahammed R. State-of-the-art recent developments of large magnetorheological (MR) dampers: A review[J]. Korea-Australia Rheology Journal, 2022, 34(2): 105-136. [17] Wu J, Zhou H, Liu Z, et al. A load-dependent PWA-H∞ controller for semi-active suspensions to exploit the performance of MR dampers[J]. Mechanical Systems and Signal Processing, 2019, 127: 441-462. [18] Zhang B, Liu M, Wang K, et al. Takagi–Sugeno Fuzzy Model-Based Control for Semi-Active Cab Suspension Equipped with an Electromagnetic Damper and an Air Spring[J]. Machines, 2023, 11(2): 226. [19] 赵丰. 基于路面感知的车辆智能悬架控制策略研究[D]. 北京理工大学, 2016. ZHAO Feng. Research on Intelligent Suspension Control Strategy for Vehicles Based on Road Surface Sensing [D]. Beijing University of Technology, 2016. [20] 邱香. 具有不确定性的主动悬架鲁棒综合控制研究[J]. 计算机仿真, 2016, 33(06): 143-148. QIU Xiang. Research on robust integrated control of active suspension with uncertainty[J]. Computer Simulation, 2016, 33(06): 143-148. [21] Ruiz-Velázquez E, García-Rodríguez J, Quiroz G, et al. Robust μ-synthesis: Towards a unified glucose control in adults, adolescents and children with T1DM[J]. Journal of the Franklin Institute, 2020, 357(14): 9633-9653.