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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Korkut Bekiroglu, Seshadhri Srinivasan, Ethan Png, Rong Su and Constantino Lagoa, "Recursive Approximation of Complex Behaviours With IoT-Data Imperfections," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 656-667, May 2020. doi: 10.1109/JAS.2020.1003126
Citation: Korkut Bekiroglu, Seshadhri Srinivasan, Ethan Png, Rong Su and Constantino Lagoa, "Recursive Approximation of Complex Behaviours With IoT-Data Imperfections," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 656-667, May 2020. doi: 10.1109/JAS.2020.1003126

Recursive Approximation of Complex Behaviours With IoT-Data Imperfections

doi: 10.1109/JAS.2020.1003126
Funds:  This work was supported by the Building and Construction Authority through the NRF GBIC Program (NRF2015ENC-GBICRD001-057)
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  • This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect (noisy and incomplete) measurements in the internet of things (IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality (0) optimization problem, known to be NP-hard. To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe (mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning (HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.

     

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  • 1 The imperfect measurements can result from a variety of reasons which include hardware aspects such as sensor failures, noise, faults, etc., or network induced imperfections such as latencies, packet dropout, quantization, and others.
    2 The set of poles can be defined based on priori such as rise time, overshoot, etc. Only the stable poles are chosen for this paper, reasonable one for HVAC systems, but can include unstable poles.
    3 Discrete linear systems with distinct poles have two facts: i) the same poles appear in the impulse response and the initial condition response; ii) the number of distinct poles used in these two is equal to the order of the system. Hence, sparsifying the impulse response of the system (i.e., identifying the system of lowest order) is equivalent to minimizing the number of unique poles that are used in the total response, i.e., the response that includes both the response to initial conditions and the zero state response to the input.
    4 Justification of Hankel norm normalization can be found in [16], [26].
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    Highlights

    • Parsimonious recursive system update of complex non-linear and time-varying behaviors.
    • Identifiability minimum data requirement for low order systems.
    • Sparse system identification in IoT setting.

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