期刊文献+

二自由度交流主动磁轴承最小二乘支持向量机解耦控制 被引量:2

Decoupling control of 2 degrees-of-freedom alternating current active magnetic bearing using least squares support vector machines
下载PDF
导出
摘要 采用最小二乘支持向量机(LS–SVM)理论,研究了二自由度交流主动磁轴承这一多变量、非线性、强耦合的控制对象的动态解耦问题.根据主动磁轴承的基本结构,利用等效磁路法推导了悬浮力模型,建立了系统的状态方程,并对其进行可逆性分析;应用LS–SVM辨识原理推导出系统的逆模型;将逆模型与原系统串联,从而将原非线性耦合系统解耦成伪线性系统,并设计了附加控制器;采用MATLAB软件平台构建了磁轴承仿真系统,对系统进行了拟合、起浮、干扰及解耦仿真试验并进行了分析;最后构建了交流主动磁轴承实验平台,对转子起浮和解耦性能进行了实验.研究表明:采用LS–SVM逆解耦控制策略,能够实现交流主动磁轴承的动态解耦,系统具有良好的动静态性能. Using the least squares support vector machines (LS-SVM), we investigate the dynamic decoupling of the 2 degrees-of-freedom (DOF) alternating current (AC) active magnetic bearing (AMB) system which is a multivariable, nonlinear and strong coupling object. According to the basic structure of the AMB system, we use the equivalent magnetic circuit method to develop the mathematical model for the suspension forces and setup the state-space equation for the 2 DOF AC AMB system. After the invertability analysis, we derive the inverse model of the system based on the identification principle of LS-SVM. By connecting the inverse system in series with the original system, we decouple the original nonlinear system into quasi-linear systems, for which additional controllers are designed. The simulation of the AMB system is carried out on the MATLAB software platform. On this platform, we perform the tests of set up, fitting, floating, disturbing and decoupling; the results are analyzed. Finally, the experimental platform of the AMB was established, on which the suspension of the rotor and the decoupling performance test are performed. The research results show that the control strategy based on LS-SVM can realize dynamic decoupling control for the 2 DOF AC AMB system with desirable dynamic and static performances.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2013年第11期1479-1485,共7页 Control Theory & Applications
基金 江苏省自然科学基金资助项目(BK2012707) 江苏省"六大人才高峰"资助项目(2011-ZBZZ026) 江苏高校优势学科建设工程资助项目(苏政办发〔2011〕6号)
关键词 主动磁轴承 支持向量机 逆系统 解耦控制 active magnetic bearing support vector machines inverse system decoupling control
  • 相关文献

参考文献11

二级参考文献58

共引文献48

同被引文献35

  • 1孙玉坤,朱熀秋,蔡兰.三自由度混合磁悬浮轴承耦合特性[J].江苏大学学报(自然科学版),2006,27(4):342-346. 被引量:16
  • 2孙玉坤,吴建兵,项倩雯.基于有限元法的磁悬浮开关磁阻电机数学模型[J].中国电机工程学报,2007,27(12):33-40. 被引量:71
  • 3吴德会.基于最小二乘支持向量机的传感器非线性动态补偿[J].仪器仪表学报,2007,28(6):1018-1023. 被引量:24
  • 4LI Y, MAW K, LIU J, et al. Design of a temperature measurement system using LabVIEW virtual instrument modules[ C]. International Conference on Advanced De- sign and Manufacturing Engineering (ADME), 2011: 1308-1313.
  • 5WANG X N, SONG S D, YU Q X. Temperature measur- ing instrumentation based on optical fiber extrinsic Fabry- Perot interferometer [ C ]. The 6th International Symposi- um on Test and Measurement (ISTM), 2005: 3165-3167.
  • 6LI Z H, LI J Z, BAO C C, et al. Automatic calibrationsystem of the temperature instrument display based on computer vision measuring [ C ]. International Conference on Display and Photonics,2010:77d908-7.
  • 7VAPNIK V. The nature of statistical learning theory [ M ]. New York: Springer Science & Business Media, 2013.
  • 8MIRANIAN A, ABDOLLAHZADE M. Developing a local least-squares support vector machines based on neuro- fuzzy model for nonlinear and chaotic time series predic- tion [ C 3. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(2) :207-218.
  • 9MELLIT A, PAVAN A M, BENGHANEM M. Least squares support vector machine for short-term prediction of meteorological time series[ J]. Theoretical and Applied Climatology, 2013, 111 (1-2) :297-307.
  • 10SUYKENS J A K, VANDEWALLE J. Least squares sup- port vector machine classifiers [ J ]. Neural Processing Letters, 1999, 9(3): 293-300.

引证文献2

二级引证文献29

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部