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个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:机械工程学院
学科:机械电子工程. 机械制造及其自动化
办公地点:机械工程学院知方楼5051
联系方式:座机:0411-84707276
电子邮箱:hbliu@dlut.edu.cn
Adaptive filtered x-least mean square algorithm with improved convergence for resonance suppression
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论文类型:期刊论文
发表时间:2014-10-01
发表刊物:PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING
收录刊物:SCIE、EI、Scopus
卷号:228
期号:9
页面范围:668-676
ISSN号:0959-6518
关键字:Resonance suppression; adaptive filtered x-least mean square algorithm; convergence speed; elastic drive systems
摘要:The existence of the resonance is usually a trouble causing instability for most elastic drive systems. Generally, the measurement of original resonance of load side in a drive system is a direct solution for resonance suppression, but exact data are difficult to come by, such as torsional torque, load speed and disturbance torque. Therefore, a developed method for resonance suppression based on adaptive filtered x-least mean square algorithm with improved convergence is presented in this research. The proposed method obtains the resonance iteratively and reduces the significant resonance oscillations through a finite impulse response filter adapted by the least mean square error principle. In order to tackle the convergence speed problem caused by the high dynamics of the forward path model, another finite impulse response filter is inserted into the control structure to smooth the current control reference signal and the speed error signal, so that the dynamics features of the forward path model are improved. Furthermore, a filtered x-least mean square control structure for elastic drive systems is also developed. From the simulation and experimental results, the resonance is more effectively suppressed with proposed modified filtered x-least mean square structure compared with notch filters, and the inserted finite impulse response improves the convergence speed.