Model Order Reduction and Distribution for Efficient State Estimation in Sensor and Actuator Networks

Volume 7, Issue 5, Page No 146-156, 2022

Author’s Name: Ferdinand Friedrich^a), Christoph Ament

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University of Augsburg, Chair of Control Theory, Augsburg, 86159, Germany

^a)whom correspondence should be addressed. E-mail: ferdinand.friedrich@uni-a.de

Adv. Sci. Technol. Eng. Syst. J. 7(5), 146-156 (2022); DOI: 10.25046/aj070516

Keywords: Model Order Reduction, Sensor and Actuator Network, Distributed State Estimation

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Abstract

We present in this contribution the distribution of a global multi-input-multi-output system in a sensor and actuator network. Based on controllability and observability, the global system is decentralized and the system properties are preserved as a result. This results in multiple decentralized local single-input-single-output systems with the same system order as the global system. As these local systems are implemented on decentralized CPUs in the network, the computational effort of the nodes has to be minimized. This is achieved by approximating the input and output behavior and reducing the system order of the decentralized local systems. For this purpose, the two most common techniques, Balanced Truncation and Krylov subspace methods, are presented. Kalman filters are used for state reconstruction. To approximate the input/output behavior of the global system, information from all decentralized reduced local systems is necessary, thus a fully interconnected network is used for communication. By decentralized fusion algorithms in the network nodes, the Kalman filter algorithm is separated and distributed in the network.

Received: 31 August 2022, Accepted: 04 October 2022, Published Online: 25 October 2022

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Model Order Reduction and Distribution for Efficient State Estimation in Sensor and Actuator Networks