Limited-communication distributed model predictive control for output feedback coupled subsystems

Jalal, Rawand E.; Rasmussen, Bryan P.

doi:10.1109/acc.2016.7525469

Cited by 3 publications

(1 citation statement)

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“…DKFs were also proposed in other fields primarily to facilitate distributed control. A DKF formulation that facilitates distributed MPC is proposed in [13], however the effect of interactions between subsystems is neglected in the update stage of KF.…”

Section: Introductionmentioning

confidence: 99%

Distributed state estimation through co‐acting Kalman filters

Kanagala

Detroja

2020

Asian Journal of Control

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Distributing calculations of a central Kalman filter requires subsystem level expressions for the propagation and update steps of the Kalman filter. It is difficult to obtain subsystem level expressions due to the inverse term present in the update step. In this manuscript, a non-iterative way of decomposing the inverse of a matrix is presented. This decomposition allows rewriting the update equations of the Kalman filter subsystem-wise. Subsequently, a Co-acting Kalman Filter (CoKF) is proposed using these decomposed central Kalman filter equations to perform distributed state estimation. The convergence of the CoKF algorithm is established under the assumption that each subsystem is observable. Two variants of the proposed CoKF, namely (m-CoKF and p-CoKF), suitable for applications on opposite ends of computation and communication resource spectrum, are presented along with the trade-offs involved. A comparison of the proposed method with existing distributed Kalman filters is also presented. The proposed CoKF algorithm is implemented on a standard wireless sensor network example with 200 nodes. The simulation results demonstrate the accuracy of the proposed CoKF algorithm relative to the central Kalman filter. KEYWORDS distributed state estimation, distributed systems, Kalman filter 1. It should distribute the calculations of a central estimator in such a way that the computations involved in central estimation are equally distributed among each distributed estimator 2. It needs to duly consider the interactions betweensubsystems and systematically incorporate information received from other subsystems 3. While keeping the communication between distributed estimators to a minimum, the estimates produced by

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Section: Introductionmentioning

confidence: 99%