Distributed Synthesis of Local Controllers for Networked Systems with Arbitrary Interconnection Topologies
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Abstract (arXiv)
We consider the problem of designing distributed controllers to guarantee dissipativity of a networked system comprised of dynamically coupled subsystems. We require that the control synthesis is carried out locally at the subsystem-level, without explicit knowledge of the dynamics of other subsystems in the network. We solve this problem in two steps. First, we provide distributed subsystem-level dissipativity analysis conditions whose feasibility is sufficient to guarantee dissipativity of the networked system. We then use these conditions to synthesize controllers locally at the subsystem-level, using only the knowledge of the dynamics of that subsystem, and limited information about the dissipativity of the subsystems to which it is dynamically coupled. We show that the subsystem-level controllers synthesized in this manner are sufficient to guarantee dissipativity of the networked dynamical system. We also provide an approach to make this synthesis compositional, that is, when a new subsystem is added to an existing network, only the dynamics of the new subsystem, and information about the dissipativity of the subsystems in the existing network to which it is coupled are used to design a controller for the new subsystem, while guaranteeing dissipativity of the networked system including the new subsystem. Finally, we demonstrate the application of this synthesis in enabling plug-and-play operations of generators in a microgrid by extending our results to networked switched systems.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2019.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2024ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networkscited by 10
- 2023Learning Dissipative Neural Dynamical Systemscited by 5
- 2022Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systemscited by 2
- 2026Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systemscited by 0
- 2026Model-Based and Data-Driven Hierarchical Control and Topology Co-Design for Robust Networked Systems10 Jun 2026
- 2024ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks5 Apr 2024
- 2023Learning Dissipative Neural Dynamical Systems27 Sep 2023
- 2022Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems15 Jul 2022