Variable Universe Fuzzy Control of 3-DOF Semi-active Seat Suspension System using Magnetorheological Damper
-
摘要: 对座椅悬架系统用磁流变阻尼器进行阻尼特性试验，利用最小二乘法对双曲正切模型进行参数辨识。结合座椅悬架系统的动力学特性，建立三自由度半主动座椅悬架系统模型。针对采用传统模糊控制精度不高的问题，提出一种基于模糊推理的变论域模糊控制策略。以脉冲路面激励和随机路面激励为输入，分别对被动悬架、传统模糊控制半主动悬架系统及变论域模糊控制半主动悬架系统进行动力学仿真分析。仿真结果表明：采用最小二乘法辨识出的参数模型可满足后续计算。所设计的变论域模糊控制策略减振效果明显优于传统模糊控制，能有效隔离路面冲击干扰，使得座椅悬架系统的综合性能得到明显改善。Abstract: The damping characteristics of magnetorheological damper used for the seat suspension system were tested, and the parameters of hyperbolic tangent model were identified with least square method. The 3-DOF model of semi-active seat suspension system was established by combining with the dynamic characteristic of seat suspension. Aiming at the problem of low precision of traditional fuzzy control, a variable universe fuzzy control based on the fuzzy reasoning was proposed. Taking the random road response and impulse road response as input excitations, the simulation analysis of the control effects of passive suspension system, traditional fuzzy control semi-active suspension system and variable universe fuzzy control semi-active suspension system were carried out, respectively. The results show that the parameters and the model identified with least square method can satisfy the subsequent calculation. And the vibration reduction effects of the variable universe fuzzy control strategy are obviously better than that of traditional fuzzy control, which can effectively isolate the impacts interference of the road surface and improve the comprehensive performance of the seat suspension system.
-
表 1 双曲正切模型参数辨识结果
电流/A α β δ c k f0 0 67.89 0.310 7 1.637 2 0.58 1.67 -6.30 0.25 249.2 0.310 7 1.637 2 1.49 1.69 -6.71 0.5 430.6 0.310 7 1.637 2 2.39 1.71 -7.12 0.75 611.9 0.310 7 1.637 2 3.31 1.73 -7.53 1 793.4 0.310 7 1.637 2 4.21 1.75 -7.93 1.25 974.8 0.310 7 1.637 2 5.12 1.77 -8.34 
下载: 导出CSV
表 2 三自由度半主动座椅悬架系统相关参数
参数 数值 ms 80 kg mv 400 kg mt 40 kg ks 8 000 N/m kv 15 800 N/m kt 158 000 N/m cs 250 N·s/m cv 1 500 N·s/m
下载: 导出CSV
表 3 控制电流I模糊规则表
I EC NB NM NS ZE PS PM PB E NB L ML M S S SL ML NM SL S SM ZE SM M SL NS M SM ZE SM SM S M ZE S SM SM ZE ZE SM S PS SM SM ZE SM ZE S M PM SL M S S M SL ML PB L ML SL M M SL L
下载: 导出CSV
表 4 伸缩因子α1(x1)模糊规则表
α1(x1) EC NB NM NS ZE PS PM PB E NB L L M S M L L NM L M S S S M L NS M M S ZE ZE M M ZE M S ZE ZE ZE S M PS M M S ZE S M M PM L M S S S M L PB L L M S M L L
下载: 导出CSV
表 5 伸缩因子α2(x2)模糊规则表
α2(x2) EC NB NM NS ZE PS PM PB E NB L L M M M L L NM L L S S S L L NS M M S ZE ZE M M ZE M S ZE ZE ZE S M PS M M S ZE S M M PM L B S S S L L PB L B M M M L L
下载: 导出CSV
表 6 伸缩因子β(y)模糊规则表
β(y) EC NB NM NS ZE PS PM PB E NB L M S ZE S M L NM L L M S M L L NS L M S S S M L ZE M M S ZE ZE M M PS M S ZE ZE ZE S M PM M M S ZE S M M PB L M S S S M L
下载: 导出CSV
表 7 脉冲路面座椅悬架性能评价指标响应峰值
控制类型 座椅加速度/(m·s-2) 悬架动行程/mm 被动座椅悬架 5.194 0 60.8 传统模糊控制 4.299 6 36.9 变论域模糊控制 3.466 7 29.5
下载: 导出CSV
表 8 随机路面座椅悬架性能评价指标均方根值
控制类型 座椅加速度/(m·s-2) 悬架动行程/mm 被动座椅悬架 0.424 3 3.5 传统模糊控制 0.360 5 2.3 变论域模糊控制 0.298 7 1.9
下载: 导出CSV
-
[1] 宁东红, 贾志娟, 董明明.车辆座椅悬架减振系统研究进展[J].中国计量大学学报, 2018, 29(2):113-120 doi: 10.3969/j.issn.2096-2835.2018.02.001Ning D H, Jia Z J, Dong M M, et al. Research progress about the vibration control system of vehicle seat suspension[J]. Journal of China Jiliang University, 2018, 29(2):113-120(in Chinese) doi: 10.3969/j.issn.2096-2835.2018.02.001 [2] Guclu R. Fuzzy logic control of seat vibrations of a non-linear full vehicle model[J]. Nonlinear Dynamics, 2005, 40(1):21-34 doi: 10.1007/s11071-005-3815-7 [3] Mcmanus S J, Clair K A S, Boileau P E', et al. Evaluation of vibration and shock attenuation performance of a suspension seat with a semi-active magnetorheological fluid damper[J]. Journal of Sound and Vibration, 2002, 253(1):313-327 doi: 10.1006/jsvi.2001.4262 [4] 郭迎庆, 徐赵东, 贾树岷, 等.磁流变智能结构的微粒群优化控制[J].振动与冲击, 2011, 30(9):59-63 doi: 10.3969/j.issn.1000-3835.2011.09.013Guo Y Q, Xu Z D, Jia S M, et al. PSO control used in magnetorheological intelligent structures[J]. Journal of Vibration and Shock, 2011, 30(9):59-63(in Chinese) doi: 10.3969/j.issn.1000-3835.2011.09.013 [5] Oh H U. Experimental demonstration of an improved magneto-rheological fluid damper for suppression of vibration of a space flexible structure[J]. Smart Materials and Structures, 2004, 13(5):1238-1244 doi: 10.1088/0964-1726/13/5/028 [6] Li W H, Liu B, Kosasih P B, et al. A 2-DOF MR actuator joystick for virtual reality applications[J]. Sensors and Actuators A: Physical, 2007, 137(2):308-320 doi: 10.1016/j.sna.2007.03.015 [7] 孟小杰, 于海龙, 笍筱亭, 等.磁流变阻尼器建模及在座椅减振中应用[J].噪声与振动控制, 2017, 37(1):58-62 http://d.old.wanfangdata.com.cn/Periodical/zsyzdkz201701013Meng X J, Yu H L, Rui X T, et al. MRD modeling and its application in seat cushioning[J]. Noise and Vibration Control, 2017, 37(1):58-62(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zsyzdkz201701013 [8] 李三妞.基于磁流变液拖拉机半主动座椅悬架减振系统研究[D].河南洛阳: 河南科技大学, 2015Li S N. Research of tractor semi-active seat suspension system based on magneto-rheological damper[D]. Henan Luoyang: Henan University of Science and Technology, 2015(in Chinese) [9] 李洪兴.变论域自适应模糊控制器[J].中国科学, 1999, 29(1):32-42 http://d.old.wanfangdata.com.cn/Periodical/zgkx-ce199901006Li H X. Adaptive fuzzy controllers based on variable universe[J]. Science in China Series E: Technological Sciences, 1999, 42(1):10(in Chinese) http://d.old.wanfangdata.com.cn/Periodical/zgkx-ce199901006 [10] Kwok N, Ha Q, Nguyen T, et al. A novel hysteretic model for magnetorheological fluid damper and parameter identification using particle swarm optimization[J]. Sensors & Actuators A Physical, 2006, 132(2):441-451 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=278246b4bdd939c74bb37adbfcc3577d [11] 陈云微.基于滑模变结构控制的车辆半主动座椅悬架研究[D].哈尔滨: 东北林业大学, 2012Chen Y W. Research on vehicle semi-active seat suspension based on sliding mode variable structure control[D]. Harbin: Northeast Forestry University, 2012(in Chinese) [12] 禚帅帅.D级轿车磁流变半主动悬架状态灰预测模糊控制研究[D].长春: 吉林大学, 2015Zhuo S S. Magnetorheological semi-active suspension control strategy with grey prediction and fuzzy for D class vehicle[D]. Changchun: Jilin University, 2015(in Chinese) [13] 郑帅.MR-SAS与EPS变论域模糊PID集成控制[D].安徽淮南: 安徽理工大学, 2015Zheng S. MR-SAS and EPS variable universe fuzzy PID integrated control[D]. Anhui Huainan: Anhui University of Science and Technology, 2015(in Chinese) [14] Nguyen S D, Nguyen Q H, Choi S B. A hybrid clustering based fuzzy structure for vibration control-Part 2: an application to semi-active vehicle seat-suspension system[J]. Mechanical Systems and Signal Processing, 2015, 56-57:288-301 doi: 10.1016/j.ymssp.2014.10.019 [15] Rath J J, Veluvolu K C, Defoort M. Adaptive super-twisting observer for estimation of random road excitation profile in automotive suspension systems[J]. The Scientific World Journal, 2014, 2014:203416 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Doaj000003736753 -
点击查看大图
图(10) / 表(8)
计量
- 文章访问数: 513
- HTML全文浏览量: 286
- PDF下载量: 30
- 被引次数: 0
