Sliding Mode Variable Structure Optimization Control of Vehicle Magneto-rheological Semi-active Suspension
-
摘要: 对悬架系统所用磁流变阻尼器进行阻尼特性试验，利用Levenberg-Marquardt优化算法对磁流变可调Sigmoid模型进行参数辨识，运用最小二乘法对辨识的参数进行拟合; 基于天棚阻尼系统，设计了四分之一车辆悬架系统滑模控制器；采用极点配置法确定切换面参数, 使用饱和函数代替符号函数，缓解滑模控制系统抖振问题, 运用模糊控制、RBF神经网络对半主动悬架滑模控制器进行优化; 以随机路面激励作为输入, 分别对模糊控制、RBF神经网络优化的滑模控制器半主动悬架进行仿真分析。仿真结果表明：该优化算法辨识的可调Sigmoid模型具有良好的控制性能，利用该模型可实现对阻尼力的准确控制, 所设计的RBF优化滑模控制器具有比模糊滑模控制器更优异的性能。Abstract: The damping characteristic test of the magnetorheological damper used in the suspension system is performed. The Levenberg-Marquardt optimization algorithm is used to identify the parameters of the adjustable Sigmoid model of the magnetorheological damper. The identified parameters are fitted using the least square method. A sliding mode controller for a quarter-vehicle suspension system is designed; the switching surface parameters are determined using the pole placement method. To alleviate the chattering problem of the sliding mode control system, the saturation function is used instead of the symbolic function. Fuzzy control and RBF neural network are used to optimize sliding mode controller of semi-active suspension. With the input of a random road excitation, the simulation analysis of the semi-active suspension with the optimized sliding mode controller is performed. Simulation results show that the adjustable Sigmoid model can achieve a more precise control of the damping force, and this model identified by the optimization algorithm can control the quarter-vehicle suspension system well. Moreover, RBF optimized sliding mode controller has better performance than fuzzy sliding mode controller.
-
表 1 可调Sigmoid模型参数辨识结果
电流/A Fm a k C0 f0/N 0.4 153.85 0.58 0.84 1.73 -5.44 0.8 328.82 0.59 0.66 2.95 -4.41 1.2 531.05 0.64 0.43 5.55 -6.43 1.6 750.89 0.66 0.50 7.37 -10.16 2.0 1055.18 0.84 0.77 8.34 -18.47 
下载: 导出CSV
表 2 悬架系统及路面激励各项参数
参数名称 数值 簧载质量mu 400 kg 非簧载质量md 40 kg 悬架刚度K 15 800 N/m 轮胎阻尼Kt 158 000 N/m 阻尼系数Cur 3 550 N/(m/s) 阻尼系数Cdr 3 550 N/(m/s) 悬架刚度Kr 15 800 N/m 下截止频率f0 0.1 Hz 车速U0 20 m/s 路面不平度G0 64×10-6 m3/cycle
下载: 导出CSV
表 3 模糊控制规则表
E EC NB NM ZO PM PB NB PB PM PM PM NB NM PM PM NM PM NB ZO NM ZO ZO ZO PM PM NB NM NM PM PM PB NB NM PM PM PB
下载: 导出CSV
表 4 不同控制策略悬架性能指标均方根
控制策略类型 性能指标 簧载速度/ (m·s-1) 簧载加速度/ (m·s-2) 悬架速度/ (m·s-1) RBF滑模控制 0.009 1 0.055 8 0.189 1 模糊滑模控制 0.011 4 0.098 4 0.200 1 被动悬架 0.013 6 0.110 0 0.183 4
下载: 导出CSV
-
[1] 刘波. 车用磁流变阻尼器的逆模型及其励磁电流驱动器研究[D]. 合肥: 合肥工业大学, 2016LIU B. Study on an inverse model of magnetorheological damper for automobiles and its current driver[D]. Hefei: Hefei University of Technology, 2016 (in Chinese) [2] 姚嘉凌, 郑加强, 高维杰, 等. 多项式模型磁流变减振器半主动悬架滑模控制[J]. 系统仿真学报, 2009, 21(8): 2400-2404 https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ200908066.htmYAO J L, ZHENG J Q, GAO W J, et al. Sliding mode control of vehicle semi-active suspension with magnetorheolog- ical dampers having polynomial model[J]. Journal of System Simulation, 2009, 21(8): 2400-2404 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ200908066.htm [3] CHAUDHURI A, WERELEY N M, RADHAKRISHNAN R. Genetic algorithms for rheological parameter estimation of magnetorheological fluids[C]//Proceedings of SPIE 5761, Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics. San Diego, California, United States: SPIE, 2005 [4] 赵强, 张娜. 基于半车解耦的半主动悬架模糊滑模控制[J]. 噪声与振动控制, 2016, 36(5): 59-64 https://www.cnki.com.cn/Article/CJFDTOTAL-ZSZK201605014.htmZHAO Q, ZHANG N. Fuzzy sliding mode control of semi-active suspension based on half-car decoupling[J]. Noise and Vibration Control, 2016, 36(5): 59-64 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZSZK201605014.htm [5] 庞辉, 杨军杰, 刘雪. 基于T-S模糊模型的主动悬架滑模容错控制器设计[J]. 工程力学, 2019, 36(2): 229-238, 248 https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201902024.htmPANG H, YANG J J, LIU X. Sliding-mode fault tolerant controller design for vehicle active suspension systems based on T-S fuzzy model[J]. Engineering Mechanics, 2019, 36(2): 229-238, 248 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX201902024.htm [6] 郭存涵, 苏小平, 缪小冬. 模糊分数阶滑模控制的主动横向稳定杆算法[J]. 机械科学与技术, 2019, 38(7): 1019-1023 doi: 10.13433/j.cnki.1003-8728.2019.20180271GUO C H, SU X P, MIAO X D. Fuzzy fraction sliding mode control method for active anti-roll bar in vehicle steering system[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(7): 1019-1023 (in Chinese) doi: 10.13433/j.cnki.1003-8728.2019.20180271 [7] 胡国良, 刘前结, 李刚, 等. 磁流变阻尼器可调Sigmoid力学模型仿真分析[J]. 机床与液压, 2016, 44(24): 9-15 doi: 10.3969/j.issn.1001-3881.2016.24.002HU G L, LIU Q J, LI G, et al. Simulation and analysis of adjustable Sigmoid model for a typical magnetorheological damper[J]. Machine Tool & Hydraulics, 2016, 44(24): 9-15 (in Chinese) doi: 10.3969/j.issn.1001-3881.2016.24.002 [8] 刘前结. 基于MRD的1/4车辆半主动悬架系统混合控制研究[D]. 南昌: 华东交通大学, 2018LIU Q J. Research on hybrid control of semi-active quarter vehicle suspension system based on magnetorheological damper[D]. Nanchang: East China Jiaotong University, 2018 (in Chinese) [9] 陈双, 赵凯旋, 屈小贞. 基于模糊切换增益调节的半主动悬架滑模控制[J]. 辽宁工业大学学报, 2019, 39(2): 96-100 https://www.cnki.com.cn/Article/CJFDTOTAL-LNGX201902006.htmCHEN S, ZHAO K X, QU X Z. Sliding-mode control semi-active of suspension based on fuzzy switching gain adjustment[J]. Journal of Liaoning University of Technology, 2019, 39(2): 96-100 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-LNGX201902006.htm [10] WANG X, ZHU W L, SONG C, et al. Chattering free sliding mode pitch control of PMSG wind turbine[J]. IFAC Proceedings Volumes, 2014, 47(3): 6758-6763 doi: 10.3182/20140824-6-ZA-1003.01220 [11] HOSEINI S M, KOSHKOUEI A J, FARROKHI M. Chattering free fuzzy sliding mode controllers for robot manipulators[C]//UKACC International Conference on CONTROL 2010. coventry, UK: IEEE, 2010 [12] 金鸿章, 罗延明, 肖真, 等. 抑制滑模抖振的新型饱和函数法研究[J]. 哈尔滨工程大学学报, 2007, 28(3): 288-291 doi: 10.3969/j.issn.1006-7043.2007.03.009JIN H Z, LUO Y M, XIAO Z, et al. Investigation of a novel method of saturation function for chattering reduction of sliding mode control[J]. Journal of Harbin Engineering University, 2007, 28(3): 288-291 (in Chinese) doi: 10.3969/j.issn.1006-7043.2007.03.009 [13] ZHANG Y M. An accurate and stable RBF method for solving partial differential equations[J]. Applied Mathematics Letters, 2019, 97: 93-98 doi: 10.1016/j.aml.2019.05.021 -
点击查看大图
图(13) / 表(4)
计量
- 文章访问数: 151
- HTML全文浏览量: 25
- PDF下载量: 26
- 被引次数: 0
