Model Order Reduction of Continuous Interval Systems via Time Moment Matching and Pole Clustering Approaches
Dharma Pal Singh Chauhan1, V. P. Singh2
1Dharma Pal Singh Chauhan, Research Scholar, Department of Electrical Engineering, National Institute of Technology, Raipur (Chhattisgarh), India.
2V. P. Singh, Assistant Professor, Department of Electrical Engineering, National Institute of Technology, Raipur (Chhattisgarh), India.
Manuscript received on 24 September 2018 | Revised Manuscript received on 30 September 2018 | Manuscript published on 30 November 2018 | PP: 313-317 | Volume-7 Issue-4, November 2018 | Retrieval Number: E1850017519©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this manuscript, authors propose an algorithm for model order reduction of interval systems. Algorithm utilizes pole clustering and time-moment matching approaches for calculating denominator and numerator of model respectively. In pole clustering approach, poles of the high order system are considered for denominator calculation. According to order of the model, clusters of the poles are framed. Cluster centre of every cluster is determined using inverse distance criterion. Using these cluster centres, model denominator is deduced. Numerator is obtained by equating time moments of system and model. Proposed algorithm is applied on sixth order system and results are compared with other existing techniques which shows that proposed algorithm is superior to other techniques.
Keywords: Inverse Distance Criterion; Interval System; Model-order Reduction; Pole-Clustering; Time-Moment; Time Moment Matching.
Scope of the Article: Clustering