Controllability of networked MIMO systems
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Abstract (arXiv)
In this paper, we consider the state controllability of networked systems, where the network topology is directed and weighted and the nodes are higher-dimensional linear time-invariant (LTI) dynamical systems. We investigate how the network topology, the node-system dynamics, the external control inputs, and the inner interactions affect the controllability of a networked system, and show that for a general networked multi-input/multi-output (MIMO) system: 1) the controllability of the overall network is an integrated result of the aforementioned relevant factors, which cannot be decoupled into the controllability of individual node-systems and the properties solely determined by the network topology, quite different from the familiar notion of consensus or formation controllability; 2) if the network topology is uncontrollable by external inputs, then the networked system with identical nodes will be uncontrollable, even if it is structurally controllable; 3) with a controllable network topology, controllability and observability of the nodes together are necessary for the controllability of the networked systems under some mild conditions, but nevertheless they are not sufficient. For a networked system with single-input/single-output (SISO) LTI nodes, we present precise necessary and sufficient conditions for the controllability of a general network topology.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 May 2015.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022A Learning Convolutional Neural Network Approach for Network Robustness Predictioncited by 6
- 2023SPP-CNN: An Efficient Framework for Network Robustness Predictioncited by 1
- 2023A Graph Transformer-Driven Approach for Network Robustness Learningcited by 1
- 2026High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approachcited by 0
- 2026High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach28 Feb 2026
- 2023A Graph Transformer-Driven Approach for Network Robustness Learning12 Jun 2023
- 2023SPP-CNN: An Efficient Framework for Network Robustness Prediction13 May 2023
- 2022A Learning Convolutional Neural Network Approach for Network Robustness Prediction20 Mar 2022