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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].
  • [1]
    J. Liu and H. Xiao, “Distributed output feedback model predictive control for a team of coupled linear subsystems,” IET Control Theory & Applications, vol. 11, no. 11, pp. 1807–1812, 2017. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2016.0751
    L. D. Xu, W. He, and S. Li, “Internet of things in industries: a survey,” IEEE Trans. Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, 2014. doi: 10.1109/TII.2014.2300753
    H. Dibowski, J. Ploennigs, and K. Kabitzsch, “Automated design of building automation systems,” IEEE Trans. Industrial Electronics, vol. 57, no. 11, pp. 3606–3613, 2010. doi: 10.1109/TIE.2009.2032209
    Y.-L. Chang and C.-C. Tsai, “Adaptive generalised predictive temperature control for air conditioning systems,” IET Control Theory & Applications, vol. 5, no. 6, pp. 813–822, 2011. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2010.0085
    D. Li, Y. Zhou, G. Hu, and C. J. Spanos, “Handling incomplete sensor measurements in fault detection and diagnosis for building hvac systems,” IEEE Trans. Automation Science and Engineering, pp. 1–14, 2019. doi: 10.1109/TASE.2019.2948101
    P. Van Overschee and B. De Moor, “N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems,” Automatica, vol. 30, no. 1, pp. 75–93, 1994. doi: 10.1016/0005-1098(94)90230-5
    G. Mercère, M. Lovera, L. Bako, and S. Lecoeuche, “Recursive subspace identification of Hammerstein models based on least squares support vector machines,” IET Control Theory & Applications, vol. 3, no. 9, pp. 1209–1216, 2009. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2008.0339
    B. Recht, M. Fazel, and P. A. Parrilo, “Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization,” SIAM Review, vol. 52, no. 3, pp. 471–501, 2010. doi: 10.1137/070697835
    M. Sznaier, M. Ayazoglu, and T. Inanc, “Fast structured nuclear norm minimization with applications to set membership systems identification,” IEEE Trans. Automatic Control, vol. 59, no. 10, pp. 2837–2842, 2014. [Online]. Available: http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6778049
    Z. Liu and L. Vandenberghe, “Interior-point method for nuclear norm approximation with application to system identification,” SIAM J. Matrix Analysis and Applications, vol. 31, no. 3, pp. 1235–1256, 2009.
    K. Bekiroglu, S. Srinivasan, E. Png, R. Su, K. Poolla, and C. Lagoa, “An internet of things compliant model identification methodology for smart buildings,” in Proc. IEEE 56th Annual Conf. Decision and Control, 2017, pp. 4440–4445.
    P. Van Dooren, K. Gallivan, and P.-A. Absil, “H2-optimal model reduction with higher-order poles,” SIAM J. Matrix Analysis and Applications, vol. 31, pp. 2738, 2010. doi: 10.1137/080731591
    B. Yilmaz, K. Bekiroglu, C. Lagoa, and M. Sznaier, “A randomized algorithm for parsimonious model identification,” IEEE Trans. Automatic Control, vol. 63, no. 2, pp. 532–539, 2018. doi: 10.1109/TAC.2017.2723959
    D. L. Donoho, “Compressed sensing,” IEEE Trans. Information Theory, vol. 52, pp. 1289–1306, 2006. doi: 10.1109/TIT.2006.871582
    V. Chandrasekaran, B. Recht, P. A. Parrilo, and A. S. Willsky, “The convex geometry of linear inverse problems,” Foundations of Computational Mathematics, vol. 12, no. 6, pp. 805–849, 2012. doi: 10.1007/s10208-012-9135-7
    P. Shah, B. N. Bhaskar, G. Tang, and B. Recht, “Linear system identification via atomic norm regularization,” in Proc. IEEE 51st Conf. Decision and Control, 2012, pp. 6265–6270.
    A. Tewari, P. K. Ravikumar, and I. S. Dhillon, “Greedy algorithms for structurally constrained high dimensional problems,” Advances in Neural Information Processing Systems, pp. 882–890, 2011.
    J.-F. Cai, E. J. Candès, and Z. Shen, “A singular value thresholding algorithm for matrix completion,” SIAM J. Optimization, vol. 20, no. 4, pp. 1956–1982, 2010.
    R. K. Lim and R. W. Longman, “State-space system identification with identified Hankel matrix,” Department of Mechanical and Aerospace Engineering Technical Report No. 3045, no. 3045, pp. 1–36, 1998.
    I. Boz, K. Bekiroglu, M. Solaimanian, P. Tavassoti-Kheiry, C. Lagoa, T.-k. Pexbouhan, and C. Lagoa, “Validation of model order assumption and noise reduction method for the impact resonance testing of asphalt concrete,” J. Nondestructive Evaluation, vol. 36, no. 3, pp. 58, 2017. doi: 10.1007/s10921-017-0436-2
    L. Ljung, “System identification,” in Signal Analysis and Prediction. Springer, 1998, pp. 163–173.
    S. Baldi, S. Yuan, P. Endel, and O. Holub, “Dual estimation: constructing building energy models from data sampled at low rate,” Applied Energy, vol. 169, pp. 81–92, 2016.
    E. Png, S. Srinivasan, K. Bekiroglu, J. Chaoyang, R. Su, and K. Poolla, “An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings,” Applied Energy, vol. 239, pp. 408–424, 2019.
    N. Radhakrishnan, S. Srinivasan, R. Su, and K. Poolla, “Learning based hierarchical distributed HVAC scheduling with operational constraints,” IEEE Trans. Control Systems Technology, vol. 26, no. 5, pp. 1892–1900, 2017. doi: 10.1109/TCST.2017.2728004
    A. Aswani, N. Master, J. Taneja, V. Smith, A. Krioukov, D. Culler, and C. Tomlin, “Identifying models of HVAC systems using semiparametric regression,” in Proc. American Control Conf., 2012, pp. 3675–3680.
    K. Bekiroglu, B. Yilmaz, C. Lagoa, and M. Sznaier, “Parsimonious model identification via atomic norm minimization,” in Proc. European Control Conf., pp. 2392–2397, 2014.


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    • 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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