TY - GEN
T1 - Colliding bodies optimization-based approximants of linear-time invariant continuous-time systems
AU - Singh, Chhabindra Nath
AU - Kumar, Deepak
AU - Samuel, Paulson
AU - Gupta, Akhilesh Kumar
AU - Sreeram, Victor
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper presents a novel hybrid model reduction technique for large-scale continuous-time systems using colliding bodies optimization algorithm (CBOA). The proposed method ensures the stability of the reduced-order approximant as the stability equations are incorporated along with CBOA. Two case studies establish the efficacy of the proposed method. An extensive comparative analysis of the dynamic responses and performance indices is also shown, confirming the supremacy of the presented method over the existing methods.
AB - This paper presents a novel hybrid model reduction technique for large-scale continuous-time systems using colliding bodies optimization algorithm (CBOA). The proposed method ensures the stability of the reduced-order approximant as the stability equations are incorporated along with CBOA. Two case studies establish the efficacy of the proposed method. An extensive comparative analysis of the dynamic responses and performance indices is also shown, confirming the supremacy of the presented method over the existing methods.
UR - http://www.scopus.com/inward/record.url?scp=85144623875&partnerID=8YFLogxK
U2 - 10.1109/ANZCC56036.2022.9966957
DO - 10.1109/ANZCC56036.2022.9966957
M3 - Conference paper
AN - SCOPUS:85144623875
T3 - 2022 Australian and New Zealand Control Conference, ANZCC 2022
SP - 46
EP - 50
BT - 2022 Australian and New Zealand Control Conference, ANZCC 2022
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 2022 Australian and New Zealand Control Conference, ANZCC 2022
Y2 - 24 November 2022 through 25 November 2022
ER -