TY - GEN
T1 - Synchronization Preserving Model Reduction of Multi-Agent Network Systems by Eigenvalue Assignments
AU - Yu, Lanlin
AU - Cheng, Xiaodong
AU - Scherpen, Jacquelien M.A.
AU - Xiong, Junlin
N1 - Funding Information:
Lanlin Yu is with the Institute of Advanced Technology, Westlake Institute for Advanced Study, Westlake University, Hangzhou 310024, China. {yulanlin1992@gmail.com} Xiaodong Cheng is with Control Systems Group, Department of Electrical Engineering Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands. {x.cheng@tue.nl} Jacquelien M.A. Scherpen is with Jan C. Willems Center for Systems and Control, Engineering and Technology Institute Groningen, Faculty of Science and Engineering, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands. {j.m.a.scherpen@rug.nl} Junlin Xiong is with the Department of Automation, University of Science and Technology of China, Hefei 230026, China.{junlin.xiong@gmail.com} This work was supported in part by the National Natural Science Foundation of China Under Project 61761136005, National Natural Science Foundation of China Under Project 61773357, the Australian Research Council under grant DP190100887 and DP160104500, the University of Groningen and Chinese Scholarship Council (CSC).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - In this paper, structure preserving model reduction problem for multi-agent network systems consisting of diffusively coupled agents is investigated. A new model reduction method based on eigenvalue assignment is derived. Particularly, the spectrum of the reduced Laplacian matrix is selected as a subset of the spectrum of the original Laplacian matrix. The resulting reduced-order model retains the network protocol of diffusive couplings, and thus the synchronization property is preserved. Moreover, a concise expression for the upper-bound of the 2 approximation error is presented in the setting of a leader-follower network, and it provides a guideline to select the eigenvalues of the reduced Laplacian matrix. The effectiveness of the proposed method is finally illustrated via the application to a spacecraft network, with a comparison of performances with the graph clustering method in [1] and balanced truncation approach in [2].
AB - In this paper, structure preserving model reduction problem for multi-agent network systems consisting of diffusively coupled agents is investigated. A new model reduction method based on eigenvalue assignment is derived. Particularly, the spectrum of the reduced Laplacian matrix is selected as a subset of the spectrum of the original Laplacian matrix. The resulting reduced-order model retains the network protocol of diffusive couplings, and thus the synchronization property is preserved. Moreover, a concise expression for the upper-bound of the 2 approximation error is presented in the setting of a leader-follower network, and it provides a guideline to select the eigenvalues of the reduced Laplacian matrix. The effectiveness of the proposed method is finally illustrated via the application to a spacecraft network, with a comparison of performances with the graph clustering method in [1] and balanced truncation approach in [2].
KW - Model/Controller reduction, Networked control systems, Network analysis and control
U2 - 10.1109/CDC40024.2019.9029857
DO - 10.1109/CDC40024.2019.9029857
M3 - Conference contribution
SN - 978-1-7281-1399-9
SP - 7794
EP - 7799
BT - Proceedings of the 58th Conference on Decision and Control
PB - IEEE
T2 - 58th Conference on Decision and Control (CDC2019)
Y2 - 11 December 2019 through 13 December 2019
ER -