Localized distributed state feedback control with communication delays

Wang, Yuh Shyang; Matni, Nikolai; You, Seungil; Doyle, John C.

doi:10.1109/acc.2014.6859440

Cited by 20 publications

(28 citation statements)

References 9 publications

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“…However, a scalar ui might affect several states at the same time, and a state might be affected by several control signals as well. plant model that was considered in [18], the latter condition is automatically satisfied. For nonscalar sub-systems, we need to explicitly impose this constraint on the controller.…”

Section: State Feedback Localizabilitymentioning

confidence: 99%

“…The feasibility of (3) can be verified in a localized manner by selecting each column of R and , motivating the definition of (j, d)-reduced state and control vectors [18].…”

Section: A Local Feasibility Characterizationmentioning

confidence: 99%

“…The key observation in [18] was that a localized (and thus sparse and scalable) controller implementation does not mean that the information structure on controller K (the transfer function from measurements to control actions) needs to be sparse. By allowing controllers to…”

Section: Introductionmentioning

confidence: 99%

“…In this paper and our previous work [18], we advocate the importance of locality (i.e. sparsity in the controller and the closed-loop response) for the joint scalability of both computation and implementation.The key observation in [18] was that a localized (and thus sparse and scalable) controller implementation does not mean that the information structure on controller K (the transfer function from measurements to control actions) needs to be sparse. By allowing controllers to…”

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confidence: 99%

“…This offers a scalable design and implementation method that additionally achieves a localized closed loop performance. This paper is an extension of the work in [18]. The primary contributions are that we (1) generalize the notion of state feedback localizability to non-scalar sub-systems, (2) propose a receding-horizon like control scheme that is numerically robust to computation errors, and (3) formulate the Localized LQR (LLQR) optimal control problem, and derive its analytic solution.…”

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confidence: 99%

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Localized LQR optimal control

Wang

Matni

Doyle

2014

53rd IEEE Conference on Decision and Control

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This paper introduces a receding horizon like control scheme for localizable distributed systems, in which the effect of each local disturbance is limited spatially and temporally. We characterize such systems by a set of linear equality constraints, and show that the resulting feasibility test can be solved in a localized and distributed way. We also show that the solution of the local feasibility tests can be used to synthesize a receding horizon like controller that achieves the desired closed loop response in a localized manner as well. Finally, we formulate the Localized LQR (LLQR) optimal control problem and derive an analytic solution for the optimal controller. Through a numerical example, we show that the LLQR optimal controller, with its constraints on locality, settling time, and communication delay, can achieve similar performance as an unconstrained H 2 optimal controller, but can be designed and implemented in a localized and distributed way.

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Section: State Feedback Localizabilitymentioning

confidence: 99%

“…The feasibility of (3) can be verified in a localized manner by selecting each column of R and , motivating the definition of (j, d)-reduced state and control vectors [18].…”

Section: A Local Feasibility Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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