Method to Calculate Multi-objective and H2/H∞ Control of Vehicle Vertical Vibration System

doi:10.16182/j.issn1004731x.joss.201612014

Abstract

Abstract: Aiming at the disadvantage of single object in railway vehicle damping system,the multi-objective optimal control law of railway vehicle damping system was devised to balance shock absorption control and energy saving control. To improve the riding comfortability and reduce the energy consumption, the multi-objective H₂/H_∞ output-feedback controller based on LMI was developed for an active suspension of 6-DOF vertical vibration model of railway vehicle. Norm H₂ of the actuator output was taken as the energy consumption indexes, and norm H_∞ of the vertical vibration displacement, the acceleration and the nutation angle was taken as the riding comfortability indexes. The results show that the controller can decrease the energy consumption of the actuator effectively and at the same time reduce the riding comfortability to an acceptable level, and make the railway vehicle obtain a good performance of riding comfortability and energy consumption of the actuator.

Key words: rail vehicle, H₂/H_∞control;, riding comfort, energy consumption

CLC Number:

U270.1+1

Meng Jianjun, Bai Huan, Mou Jian, Yin Ming, Guan Xuri. Method to Calculate Multi-objective and H₂/H_∞ Control of Vehicle Vertical Vibration System[J]. Journal of System Simulation, 2016, 28(12): 2973-2978.

References

[1] 闫光辉, 关志伟, 杜峰, 等. 车辆主动悬架自适应LQG控制策略研究[J]. 机械科学与技术, 2014, 33(3): 432-437.
[2] Morteza Moradi, Afef Fekih.Adaptive PID-Sliding-Mode Fault-Tolerant Control Approach for Vehicle Suspension Systems Subject to Actuator Faults[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2014, 63(3): 1041-1054.
[3] Li H, Yu J, Hilton C, et al.Adaptive sliding mode control for nonlinear active suspension vehicle systems using TS fuzzy approach[J]. IEEE Transactions on Control Systems Technology (S1063-6536), 2013, 60(8): 393-407.
[4] Du H, Zhang N.Fuzzy control for nonlinear uncertain electrohydraulic active suspensions with input constraint[J]. IEEE Transactions on Fuzzy Systems (S1063-6706), 2009, 17(2): 343-356.
[5] 常胜, 许洪国, 刘宏飞. H_∞回路成形半挂汽车列车鲁棒控制器设计[J]. 吉林大学学报(工学版), 2011, 41(6): 571-576.
[6] 李荣, 焦晓红, 杨超. 基于动态输出反馈的半车主动悬架系统鲁棒控制[J]. 振动与冲击, 2014, 33(7): 187-193.
[7] 龙垚坤, 文桂林, 陈哲吾. 汽车主动悬架鲁棒保性能控制仿真研究[J]. 汽车工程, 2014, 36(2): 216-221.
[8] 杨柳青, 陈无畏, 汪洪波. 基于H_2/H_∞控制的汽车主动悬架最优鲁棒容错控制[J]. 中国机械工程, 2012, 23(24): 3013-3019.
[9] 李以农, 郑玲, 罗铭刚, 等. 汽车主动悬架H_2/H_∞多目标控制线性矩阵不等式方法[J]. 重庆大学学报, 2010, 33(4): 1-8.
[10] 黎松奇, 张昆仑. 基于悬浮控制算法的磁浮列车动力学仿真研究[J]. 系统仿真学报, 2015, 27(1): 179-184.
[11] 陈果, 翟婉明, 左洪福. 车辆-轨道耦合系统随机振动响应特性分析[J]. 交通运输工程学报, 2001, 1(1): 13-16.
[12] 全顺喜, 王平, 赵才友. 车辆多体系统振动方程建立探讨[J]. 振动与冲击, 2013, 32(11): 173-178.
[13] 俞立. 鲁棒控制——线性矩阵不等式处理方法 [M]. 北京: 清华大学出版社, 2002: 61-63.
[14] 王英杰, 时瑾, 魏庆朝. 单车过桥下弹性车体共振与消振现象分析[J]. 振动与冲击, 2015, 34(2): 1-8.
[15] Ravi Tandon, Soheil Mohajer, H Vincent Poor, et al. Degrees of Freedom Region of the MIMO Interference Channel with Output Feedback and Delayed CSIT[J]. IEEE Transactions on Information Theory (S0018-9448), 2013, 59(3): 1444-1457.

Method to Calculate Multi-objective and H₂/H_∞ Control of Vehicle Vertical Vibration System

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu. Multi-UAVs 3D Path Planning Method Based on Random Strategy Search [J]. Journal of System Simulation, 2022, 34(6): 1286-1295.
[2]	Yanqin Long, Guifang Qiao, Guangming Song, Ying Zhang, Linlin Cheng. Motion Simulation and Performance Analysis of 2D Variable Stiffness Snake-like Robot [J]. Journal of System Simulation, 2022, 34(4): 759-767.
[3]	Zi'an Zhao, Hong Zhou, Yingjian Lei. Modeling and Optimization for Manufacturing Cell Scheduling Based on Improved Wolf Pack Algorithm and Simulation [J]. Journal of System Simulation, 2022, 34(2): 201-211.
[4]	Yan Bai, Lulu Wu, Yin'e He, Yuying Wang. Energy Consumption Prediction for Air-conditioning System Based on Dynamic Temperature Control [J]. Journal of System Simulation, 2022, 34(2): 366-375.
[5]	Hu Rong, Dong Yuming, Qian Bin. Pathfinder Algorithm for Green Pipeline Scheduling with Limited Buffers [J]. Journal of System Simulation, 2021, 33(6): 1384-1396.
[6]	Sun Weiqing, Song He, Han Dong, Tian Kunpeng. Deep Peak-shaving Transformation Planning of Thermal Power Units Based on Approximate Dynamic Programming [J]. Journal of System Simulation, 2021, 33(6): 1415-1426.
[7]	Li Fei, Su Zhong. Intelligent Control of Wastewater Treatment Processes Based on Adaptive Immune Optimization [J]. Journal of System Simulation, 2021, 33(12): 3012-3020.
[8]	Meng Qinglong, Zhang Zhibo, Xiong Cheng. Simulation Research on Operation Effect of VAV Air-conditioning System with Variable Water Temperature Strategy [J]. Journal of System Simulation, 2019, 31(8): 1617-1626.
[9]	Wang Xiuyan, Liu Yanmin, Zhang Gewen, Li Zongshuai, Lin Jiaquan. Prediction of Aircraft Cabin Energy Consumption Based on PSO and CRO Algorithms [J]. Journal of System Simulation, 2018, 30(8): 3074-3081.
[10]	Wang Xiuyan, Liu Yanmin, Zhang Gewen, Li Zongshuai, Lin Jiaquan. Prediction of Aircraft Cabin Energy Consumption Based on Improved Cooperative PSO Neural Network [J]. Journal of System Simulation, 2018, 30(4): 1535-1541.
[11]	Hu Kezhen, Wu Jianping, Liu Mingyu. Modelling of EVs Energy Consumption from Perspective of Field Test Data and Driving Style Questionnaires [J]. Journal of System Simulation, 2018, 30(11): 4106-4114.
[12]	Zhang Xiaoxing, Wang Yan, Yan Dahu, Ji Zhicheng. Improved Shuffled Frog-Leaping Algorithm for Solving Flexible Job Shop Scheduling Problem [J]. Journal of System Simulation, 2017, 29(9): 2093-2099.
[13]	Yang Haidong, Guo Chengdun, Li Hong, Liu Guosheng, Wu Jiawen. Study of Model and Simulation on Ceramic Tile Polishing Energy Consumption [J]. Journal of System Simulation, 2016, 28(4): 765-771.
[14]	Yang Yongbo, Chen Ying, Yang Jing, Liu Mingzhong, Jiang Zhenchao, Zeng Rong. Energy Conservation and Emissions Reduction Policy Simulation about Energy-consuming Enterprises [J]. Journal of System Simulation, 2016, 28(4): 972-978.
[15]	Chen Yan, Wang Yan. Energy Consumption Analysis of Discrete Manufacturing Based on Improved Principal Component Analysis Method [J]. Journal of System Simulation, 2016, 28(12): 3087-3094.