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In this paper, we propose a distributed output-feedback controller design for a linear time-invariant plant interacting with networked agents, where interaction and communication of each agent are limited to its associated input-output channel and its neighboring agents, respectively. The design scheme has a decentralized structure so that each agent can self-organize its own controller using the locally accessible information only. Furthermore, under mild conditions, the proposed controller is capable of maintaining stability even when agents join/leave the network during the operation without requiring any manipulation on other agents. This plug-and-play feature leads to efficiency for controller maintenance as well as resilience against changes in interconnections. The key idea enabling these features is the use of Bass algorithm, which allows the distributed computation of stabilizing gains by solving a Lyapunov equation in a distributed manner.
We study the problem of controlling multi-agent systems under a set of signal temporal logic tasks. Signal temporal logic is a formalism that is used to express time and space constraints for dynamical systems. Recent methods to solve the control syn
The linear-quadratic controller is one of the fundamental problems in control theory. The optimal solution is a linear controller that requires access to the state of the entire system at any given time. When considering a network system, this render
The modern power system features high penetration of power converters due to the development of renewables, HVDC, etc. Currently, the controller design and parameter tuning of power converters heavily rely on rich engineering experience and extrapola
This paper studies a scalable control method for multi-zone heating, ventilation and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort and indoor air quality (IAQ) (represented by CO2) simultaneously. This pr
In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed. We propose a distributed learning approach to predict the re