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Model Reduction Using Parameterized Limited Frequency Interval Gramians for 1-D and 2-D Separable Denominator Discrete-Time Systems

Created Date 7/4/2018    View Numbers  142 Return    
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时间: 2018-07-04 周三上午 10:00-11:00 报告地点:上海大学宝山校区东区机电大楼606 报告人: Victor Sreeram 教授 报告人Victor Sreeram 教授简介: Prof. Victor Sreeram obtained Bachelor's degree in 1981 from Bangalore University, India, Master's degree in 1983 from Madras University, India, and Ph.D degree from University of Victoria, Canada in 1989, all in Electrical Engineering. He worked as a Project Engineer in the Indian Space Research Organisation from 1983 to 1985. He joined the School of Electrical, Electronic, and Computer Engineering, University of Western Australia in 1990 and he is now a Professor. He has held Visiting Appointments at the Department of Systems Engineering, Australian National University during 1994, 1995 and 1996 and at the Australian Telecommunication Research Institute in Curtin University of Technology during 1997 and 1998. He is on the editorial board of many journals including IET Control, theory and applications, Asian Journal of Control, Mathematical Problems in Engineering, Journal of Industrial and Management Optimization, Cogent Engineering, and Smart Grid and Renewable Energy. He was the General Chair of 3rd Australian Control Conference, held in Perth during November 2013 and Vice Chair of Australiasian Power Engineering Conference (AUPEC) held in Perth during October 2014 and is now a Fellow of Institution of Engineers, Australia. He is currently the Chair of the steering committee for Australia New Zealand Control Conference series.His research interests are control, signal processing, communications and Smart Grid and Renewable Energy. 报告摘要: In this talk, we propose model reduction algorithms based on the frequency-domain interval Gramians for 1-D and separable denominator 2-D discrete-time systems using balanced truncation as a parameterized combination of unweighted and the limited-frequency interval Gramians. The values of free parameters are computed using a line search optimization. The proposed algorithms provide a substantial improvement in the approximation error than the well-known existing techniques and generate stable reduced models along with an easily computable error-bound. The effectiveness of proposed algorithms is validated with the help of numerical examples of a sixth-order elliptic low-pass filter and a (6, 6)-order Roesser model of a separable denominator 2-D system.


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