期刊文献+

Study on LQR control algorithm using superelement model 被引量:3

Study on LQR control algorithm using superelement model
下载PDF
导出
摘要 The conventional linear quadratic regulator(LQR) control algorithm is one of the most popular active control algorithms.One important issue for LQR control algorithm is the reduction of structure's degrees of freedom(DOF). In this work, an LQR control algorithm with superelement model is intended to solve this issue leading to the fact that LQR control algorithm can be used in large finite element(FE) model for structure. In proposed model, the Craig-Bampton(C-B) method, which is one of the component mode syntheses(CMS), is used to establish superelement modeling to reduce structure's DOF and applied to LQR control algorithm to calculate Kalman gain matrix and obtain control forces. And then, the control forces are applied to original structure to simulate the responses of structure by vibration control. And some examples are given. The results show the computational efficiency of proposed model using synthesized models is higher than that of the classical method of LQR control when the DOF of structure is large. And the accuracy of proposed model is well. Meanwhile, the results show that the proposed control has more effects of vibration absorption on the ground structures than underground structures. The conventional linear quadratic regulator (LQR) control algorithm is one of the most popular active control algorithms. One important issue for LQR control algorithm is the reduction of structure’s degrees of freedom (DOF). In this work, an LQR control algorithm with superelement model is intended to solve this issue leading to the fact that LQR control algorithm can be used in large finite element (FE) model for structure. In proposed model, the Craig-Bampton (C-B) method, which is one of the component mode syntheses (CMS), is used to establish superelement modeling to reduce structure’s DOF and applied to LQR control algorithm to calculate Kalman gain matrix and obtain control forces. And then, the control forces are applied to original structure to simulate the responses of structure by vibration control. And some examples are given. The results show the computational efficiency of proposed model using synthesized models is higher than that of the classical method of LQR control when the DOF of structure is large. And the accuracy of proposed model is well. Meanwhile, the results show that the proposed control has more effects of vibration absorption on the ground structures than underground structures.
作者 XU Qiang CHEN Jian-yun LI Jing YUAN Chen-yang ZHAO Chun-feng 徐强;陈健云;李静;苑晨阳;赵春风(School of Civil and Hydraulic Engineering, Dalian University of Technology)
出处 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第9期2429-2442,共14页 中南大学学报(英文版)
基金 Project(LZ2015022)supported by Educational Commission of Liaoning Province of China Projects(51138001,51178081)supported by the National Natural Science Foundation of China Project(2013CB035905)supported by the Basic Research Program of China Projects(DUT15LK34,DUT14QY10)supported by Fundamental Research Funds for the Central Universities,China
关键词 linear quadratic regulator (LQR) control algorithm component mode synthesis (CMS) Craig-Bampton (C-B) method superelement vibration control LQR控制 控制算法 单元模型 地面结构 振动控制 计算效率 自由度 组件模式
  • 相关文献

参考文献24

  • 1LlU X D, WU Y J, ZHANG Y, XIAO S. A control method to make Iqr robust: A planes cluster approaching mode[.I]. International Journal of Control, Automation, and Systems, 2014. 12(2): 302-308.
  • 2HUI Q. Distributed semistable LQR control for discrete-time dynamically coupled systems[fl. Journal of the Franklin Institute. 2012,349: 74-92.
  • 3TAO C W, TAUR J S, CHEN Y C. Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system[fl. Fuzzy Sets and Systems, 2010,161: 2081-2103.
  • 4TARAPAD A R, DEBABRATA C. Optimal vibration control of smart fiber reinforced composite shell structures using improved genetic algorithm[J]. .Iournal of Sound and Vibration. 2009, 319( 1/2): 15-40.
  • 5BASU B, NAGARAJAIAH S. A wavelet-based time-varying adaptive LQR algorithm for structural control[J]. Engineering Structures, 2008, 30(9): 2470-2477.
  • 6POODEH M B, ESHTEHARDIHA S, KIYOUMARSI A. ATAEI M. Optimizing LQR and pole placement to control buck converter by genetic algorithm[J]. Control, Automation and Systems, 2007, 17118119/20: 2195-2200.
  • 7POURZEYNALI S, LAVASANI H 1-1, MODARAYI A 1-1. Active control of high rise building structures using fuzzy logic and genetic algorithms[J]. Engineering Structures. 2007. 29(3): 346-357.
  • 8JIANG Z, HAN J D, WANG Y C, SONG Q. Enhanced LQR control for unmanned helicopter in hover[.I]. Systems and Control in Aerospace and Astronautics, 2006. 6: 1437-1443.
  • 9TODOROV E, SAN D C. Ll W W. A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems[J]. American Control Conference. 2005. I: 300-306.
  • 10TAYFUN c. STEPHEN P B. Global optimal feedback control for general nonlinear systems with nonquadrntic performance criteria[.I]. Systems & Control Letters. 2004, 53(5): 327-346.

同被引文献14

引证文献3

二级引证文献3

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