Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  百年期刊
Journal of Tsinghua University(Science and Technology)    2019, Vol. 59 Issue (7) : 567-574     DOI: 10.16511/j.cnki.qhdxxb.2019.21.016
AUTOMOTIVE ENGINEERING |
Verification platform for magnetorheological semi-active suspension control algorithm
PANG Qiqi1,2, ZHANG Lixia2, HE Yichao1, GONG Zheng3, FENG Zhanzong1, CHEN Yalong1, WEI Yintao1, DU Yongchang1
1. State key laboratory of automotive safety and energy, Tsinghua University, Beijing 100084, China;
2. School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China;
3. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Download: PDF(5744 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  A control algorithm verification platform was built based on a rapid prototype controller to provide fast, effective verification of magnetorheological semi-active suspension control algorithms. The kingpin angle and the space angle of the lower arm were considered in the 1/4 vehicle suspension system model. The acceleration sensor and the body height sensor were mounted on the suspension. An LMS Test Lab system was used to collect and analyze the suspension system vibration data. The rapid prototype controller was used as the carrier for developing and debugging the control algorithms. Platform verification tests were used to verify the algorithm development, the control process monitoring, the control algorithm adjustments, and the quantitative analyses. This platform meets the design goals and is convenient, fast, and effective for developing, debugging, and verifying semi-active control algorithms.
Keywords 1/4 vehicle suspension model      sensor      signal acquisition and analysis system      rapid prototype controller      algorithm verification platform     
Issue Date: 21 June 2019
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
PANG Qiqi
ZHANG Lixia
HE Yichao
GONG Zheng
FENG Zhanzong
CHEN Yalong
WEI Yintao
DU Yongchang
Cite this article:   
PANG Qiqi,ZHANG Lixia,HE Yichao, et al. Verification platform for magnetorheological semi-active suspension control algorithm[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(7): 567-574.
URL:  
http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2019.21.016     OR     http://jst.tsinghuajournals.com/EN/Y2019/V59/I7/567
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
[1] 赵云堂, 陈思忠, 冯占宗, 等. 磁流变半主动悬架的天棚控制方法研究[J]. 汽车工程学报, 2011, 01(3):127-133.ZHAO Y T, CHEN S Z, FEND Z Z, et al. Study on the dynamic simulation of magneto-rheological semi-active suspension using sky-hook control method[J]. Journal of Automotive Engineering, 2011, 01(3):127-133. (in Chinese)
[2] SAVARESIS S, POUSSOT V C, SPELTA C, et al. Semi-active suspension control for vehicles[M]. Oxford, UK:Elsevier, 2010:1-13.
[3] 魏特特. 基于ControlBase的小型煤油活塞发动机控制策略研究[D]. 南京:南京航空航天大学, 2016.WEI T T. Research on control strategy of small kerosene piston engine based on ControlBase[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2016. (in Chinese)
[4] PANG H, LIU F, XU Z R. Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization[J]. Neurocomputing, 2018, 306(6):130-140.
[5] 陈长征, 贺东宇, 左秋阳, 等. 汽车半主动悬架神经模糊融合网络控制[J]. 沈阳工业大学学报, 2014, 36(2):170-175. CHEN C Z, HE D Y, ZUO Q Y, et al. Neural-fuzzy fusion network control of vehicle semi-active suspension[J]. Journal of Shenyang University of Technology, 2014, 36(2):170-175. (in Chinese)
[6] 赵义伟. 基于磁流变减振器的铁道车辆悬挂系统半主动硬件在环试验研究[D]. 石家庄:石家庄铁道大学, 2017.ZHAO Y W. Hardware in the loop test research of railway vehicle suspension system semi-active based on the magnetorheological damper[D]. Shijiazhuang:Shijiazhuang Tiedao University, 2017. (in Chinese)
[7] KWAK M K, LEE.J H, YANG D H, et al. Hardware-in-the-loop simulation experiment for semi-active vibration control of lateral vibrations of railway vehicle by magneto-rheological fluid damper[J]. Vehicle System Dynamics:International Journal of Vehicle Mechanics and Mobility, 2014, 52(7):891-908.
[8] 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, 2015.ZHUO S S. Magnetorheological semi-active suspension control strategy with grey prediction and fuzzy for D class vehicle[D]. Changchun:Jilin University, 2015. (in Chinese)
[9] 齐鲲鹏, 隆武强, 陈雷. 硬件在环仿真在汽车控制系统开发中的应用及关键技术[J]. 内燃机, 2006(5):24-27.QI K P, LONG W Q, CHEN L. Application of hardware-in-the-loop simulation in the development of control system for vehicle and its key technologies[J]. Internal Combustion Engines, 2006(5):24-27. (in Chinese)
[10] 杨继伟. 基于DSP的汽车磁流变半主动悬架智能测控系统研究[D]. 重庆:重庆大学, 2014.YANG J W. Research on intelligent measurement and control system of automotive magnetorheological semi-active suspension based on DSP[D]. Chongqing:Chongqing University, 2014. (in Chinese)
[11] 彭志召, 张进秋, 张建, 等. 磁流变半主动悬架试验研究[J]. 汽车工程, 2018, 40(5):561-567.PENG Z Z, ZHANG J Q, ZHANG J, et al. Experimental study on a semi-active magnetorheological suspension[J]. Automotive Engineering, 2018, 40(5):561-567. (in Chinese)
[12] SONG B K, AN J H, CHOI S B. A new fuzzy sliding mode controller with a disturbance estimator for robust vibration control of a semi-active vehicle suspension system[J]. Applied Sciences, 2017, 7:1053.
[13] 彭志召, 张进秋, 张雨, 等. 车辆半主动悬挂的频域控制算法[J]. 装甲兵工程学院学报, 2013, 27(4):36-42, 50.PENG Z Z, ZHANG J Q, ZHANG Y, et al. Frequency domain control algorithm for semi-active suspension of vehicles[J]. Journal of Academy of Armored Force Engineering, 2013, 27(4):36-42, 50. (in Chinese)
[14] 任宏斌, 陈思忠, 冯占宗. 基于天棚on-off控制的磁流变半主动悬架研究[J]. 北京理工大学学报, 2014, 34(2):148-152. REN H B, CHEN S Z, FENG Z Z. The skyhook on-off control of semi-active suspension with magneto-rheological damper[J]. Transaction of Beijing Institute of Technology, 2014, 34(2):148-152. (in Chinese)
[1] LI Yanlin, QIN Benke, BO Hanliang. Analytical model and verification of capacitance rod position measurement sensor[J]. Journal of Tsinghua University(Science and Technology), 2022, 62(10): 1636-1644.
[2] SONG Xinrui, ZHANG Xianqi, ZHANG Zhan, CHEN Xinhao, LIU Hongwei. Multi-sensor data fusion for complex human activity recognition[J]. Journal of Tsinghua University(Science and Technology), 2020, 60(10): 814-821.
[3] SUN Bowen, ZHU Zhiming, GUO Jichang, ZHANG Tianyi. Detection algorithms and optimization of image processing for visual sensors using combined laser structured light[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(6): 445-452.
[4] ZHANG Xinyu, GAO Hongbo, ZHAO Jianhui, ZHOU Mo. Overview of deep learning intelligent driving methods[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(4): 438-444.
[5] YU Jinpeng, ZHOU Yan, MO Ni, LIU Xingnan, ZHAO Lei. Design of a digital eddy current displacement sensor based on FPGA[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(3): 330-336.
[6] JI Jianchao, ZHANG Yu, WANG Mingxin. Optimization of acoustic sensor arrays for wind tunnel measurements[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(1): 94-100.
[7] MA Rui, ZHU Tianbao, MA Ke, HU Changzhen, ZHAO Xiaolin. Single-witness-based distributed detection for node replication attack[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 909-913,920.
[8] WANG Jianrong, GAO Yongchun, ZHANG Ju, WEI Jianguo, DANG Jianwu. Automatic speech recognition by a Kinect sensor for a robot under ego noises[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(9): 921-925.
[9] MAN Dapeng, WANG Chenye, YANG Wu, WANG Wei, XUAN Shichang, JIN Xiaopeng. Energy-efficient cluster-based privacy data aggregation for wireless sensor networks[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(2): 213-219.
[10] XIN Zhe, ZOU Ruobing, LI Shengbo, YU Jiaying, DAI Yifan, CHEN Hailiang. Target recognition around a vehicle based on an ultrasonic sensor array[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(12): 1287-1295.
[11] LIU Yimin, WEN Junjie, WANG Lanjun. Outlier detection based on spatio-temporal nearest neighbors and a likelihood ratio test for sensor networks[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(11): 1196-1201.
[12] FU Junsong, LIU Yun. Node security model for wireless sensor networks based on a reputation system and data noise point detection technique[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 24-27.
[13] ZHOU Caiqiu, YANG Yuwang, WANG Yongjian. Behavior measurement scheme for the wireless sensor network nodes[J]. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 39-43.
[14] SUN Zhiyuan, LU Huapu. Optimal traffic sensor layout model considering traffic big data[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(7): 743-750.
[15] CAI Jie, GU Ming. Performance analysis of star topology wireless sensor networks based on IEEE 802.15.4[J]. Journal of Tsinghua University(Science and Technology), 2015, 55(5): 565-571.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © Journal of Tsinghua University(Science and Technology), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd