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Creators/Authors contains: "Kashyap, Mruganka"

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  1. ; (, IEEE Control Systems Letters)
    It is well-known that linear quadratic regulators (LQR) enjoy guaranteed stability margins, whereas linear quadratic Gaussian regulators (LQG) do not. In this letter, we consider systems and compensators defined over directed acyclic graphs. In particular, there are multiple decision-makers, each with access to a different part of the global state. In this setting, the optimal LQR compensator is dynamic, similar to classical LQG. We show that when sub-controller input costs are decoupled (but there is possible coupling between sub-controller state costs), the decentralized LQR compensator enjoys similar guaranteed stability margins to classical LQR. However, these guarantees disappear when cost coupling is introduced. 
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  2. ; (, IEEE Transactions on Automatic Control)
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  3. ; (, IEEE Conference on Decision and Control)
    We consider the problem of controlling a set of dynamically decoupled plants where the plants' subcontrollers communicate with each other according to a fixed and known network topology. We assume the communication to be instantaneous but there is a fixed processing delay associated with incoming transmissions. We provide explicit closed-form expressions for the optimal decentralized controller under these communication constraints and using standard LQG assumptions for the plants and cost function. Although this problem is convex, it is challenging due to the irrationality of continuous-time delays and the decentralized information-sharing pattern. We show that the optimal subcontrollers each have an observer-regulator architecture containing LTI and FIR blocks and we characterize the signals that subcontrollers should transmit to each other across the network. 
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  4. ; (, IEEE Conference on Decision and Control)
    We consider a dynamically decoupled network of agents each with a local output-feedback controller. We assume each agent is a node in a directed acyclic graph and the controllers share information along the edges in order to cooperatively optimize a global objective. We develop explicit state-space formulations for the jointly optimal networked controllers that highlight the role of the graph structure. Specifically, we provide generically minimal agent-level implementations of the local controllers along with intuitive interpretations of their states and the information that should be transmitted between controllers. 
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