Affine Dependence of Network Controllability/Observability on Its Subsystem Parameters and Connections
Tong Zhou, Yuyu Zhou
备注：
15 pages, 1 figure. Results without proof of Section III in this paper have been presented in the 58th IEEE Conference on Decision and Control, Nice, France, 2019. This work has been submitted to the IEEE for possible publication
发表信息：
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020
This paper investigates observability/controllability of a networked dynamic system (NDS) in which system matrices of its subsystems are expressed through linear fractional transformations (LFT). Some relations have been obtained between this NDS and descriptor systems about their observability/controllability. A necessary and sufficient condition is established with the associated matrices depending affinely on subsystem parameters/connections. An attractive property of this condition is that all the required calculations are performed independently on each individual subsystem. Except well-posedness, not any other conditions are asked for subsystem parameters/connections. This is in sharp contrast to recent results on structural observability/controllability which is proven to be NP hard. Some characteristics are established for a subsystem which are helpful in constructing an observable/controllable NDS. It has been made clear that subsystems with an input matrix of full column rank are helpful in constructing an observable NDS, while subsystems with an output matrix of full row rank are helpful in constructing a controllable NDS. These results are extended to an NDS with descriptor form subsystems. As a byproduct, the full normal rank condition of previous works on network observability/controllability has been completely removed. On the other hand, satisfaction of this condition is shown to be appreciative in building an observable/controllability NDS.
Control-Theoretic View of Neural ODEs: Empirical Controllability and Observability
The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；AI, Autonomy, Resilience, Control (AARC) Lab at the Department of Electrical and Computer Engineering, The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校电气与计算机工程系自主、韧性控制实验室）
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Energy Systems Division, Argonne National Laboratory（阿贡国家实验室能源系统部）；Department of Electrical Engineering, Guangxi University（广西大学电气工程学院）；Energy and Transportation Sciences Division, Oak Ridge National Laboratory（橡树岭国家实验室能源与交通科学部）
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Distributed Verification of Structural Controllability for Linear Time-Invariant Systems
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CONACyT - Institute of Mathematics, Universidad Nacional Autonoma de Mexico（墨西哥国立自治大学数学研究所）；Institute of Mathematics, Universidad Nacional Autonoma de Mexico（墨西哥国立自治大学数学研究所）；Laboratoire des Sciences du Numérique de Nantes, UMR CNRS 6004（南特数字科学实验室）
The School of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；Birkbeck, University of London（伦敦大学伯贝克学院）；University of Luxembourg（卢森堡大学）
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