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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2019> 28(3): 399-409
Peng Qiu, Qian Dong, Mingqian Li, Guangjie Zhai, Xueyan Wang. Compressive Measurement Identification of Linear Time-Invariant System Application in DC Motor[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(3): 399-409. doi: 10.15918/j.jbit1004-0579.18004
Citation: Peng Qiu, Qian Dong, Mingqian Li, Guangjie Zhai, Xueyan Wang. Compressive Measurement Identification of Linear Time-Invariant System Application in DC Motor[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(3): 399-409.doi:10.15918/j.jbit1004-0579.18004
shu

Compressive Measurement Identification of Linear Time-Invariant System Application in DC Motor

doi:10.15918/j.jbit1004-0579.18004
  • 1.

    Key Laboratory of Electronics and Information Technology for Space System, National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China;University of Chinese Academy of Sciences, Beijing 100049, China

  • 2.

    Key Laboratory of Electronics and Information Technology for Space System, National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    Beijing Information Science and Technology University, Beijing 100192, China

  • Received Date:2018-01-04
    Abstract
  • In traditional system identification (SI), actual values of system parameters are concealed in the input and output data; hence, it is necessary to apply estimation methods to determine the parameters. In signal processing, a signal with Nelements must be sampled at least Ntimes. Thus, most SI methods use Nor more sample data to identify a model with Nparameters; however, this can be improved by a new sampling theory called compressive sensing (CS). Based on CS, an SI method called compressive measurement identification (CMI) is proposed for reducing the data needed for estimation, by measuring the parameters using a series of linear measurements, rather than the measurements in sequence. In addition, the accuracy of the measurement process is guaranteed by a criterion called the restrict isometric principle. Simulations demonstrate the accuracy and robustness of CMI in an underdetermined case. Further, the dynamic process of a DC motor is identified experimentally, establishing that CMI can shorten the identification process and increase the prediction accuracy.
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    • References (25)
  • [1]
    Keesman K J. System identification:an introduction[M]. London:Springer-Verlag, 2011.
    [2]
    Candès E J, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans Inf Theory, 2006, 52(2):489-509.
    [3]
    Candès E J. Compressive sampling[C]//Proc Int Cong of Mathematicians, 2006:1433-1452.
    [4]
    Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Process Mag, 2008, 25(2):83-91.
    [5]
    Zhang Y D, Wang S H, Ji G L, et al. Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging[J]. IEEE Trans Electr Electron Eng, 2015, 10(1):116-117.
    [6]
    Rossi M, Haimovich A M, Eldar Y C. Spatial compressive sensing for MIMO radar[J]. IEEE Trans Signal Process, 2014, 62(2):419-430.
    [7]
    Bolstad A, Miller B A. Sparse volterra systems:theory and practice[C]//2013 IEEE Int Conf on Acoustics, Speech and Signal Processing, 2013.
    [8]
    Wang W X, Yang R, Lai Y C, et al. Predicting catastrophes in nonlinear dynamical systems by compressive sensing[J]. Phys Rev Lett, 2011, 106(15):4.
    [9]
    Toth R, Sanandaji B M, Poolla K, et al. Compressive system identification in the linear time-invariant framework[C]//50th IEEE Conf on Decision and Control and European Control Conference (CDCECC), 2011:783-790.
    [10]
    Sanandaji B M, Vincent T L, Wakin M B, et al. Compressive system identification of LTI and LTV ARX models[C]//50th IEEE Conf on Decision and Control and European Control Conference (CDCECC), 2011:791-798.
    [11]
    Sanandaji B M, Vincent T L, Wakin M B, et al. Compressive topology identification of interconnected dynamic systems via clustered orthogonal matching pursuit[C]//50th IEEE Conf on Decision and Control and European Control Conference (CDCECC), 2011:174-180.
    [12]
    Bajwa W U, Haypt J D, Raz G M, et al. Toeplitzstructured compressed sensing matrices[C]//2007 IEEE/SP 14th Workshop on Statistical Signal Processing, Vols 1 and 2, 2007:294-298.
    [13]
    Yoo J, Turnes C, Nakamura E B, et al. A compressed sensing parameter extraction platform for radar pulse signal acquisition[J]. IEEE J Em Sel Top C, 2012, 2(3):626-638.
    [14]
    Tropp J A, Wakin M B, Duarte M E, et al. Random filters for compressive sampling and reconstruction[C]//2006 IEEE Int Conf on Acoustics, Speech and Signal Processing, Vols. 1-13, 2006.
    [15]
    Qureshi M A, Deriche M. A new wavelet based efficient image compression algorithm using compressive sensing[J]. Multimed Tools Appl, 2016, 75(12):6737-6754.
    [16]
    Ma J J, Yuan X J, Ping L. On the performance of turbo signal recovery with partial DFT sensing matrices[J]. IEEE Signal Process Lett, 2015, 22(10):1580-1584.
    [17]
    Zhou N R, Yang J P, Tan C F, et al. Double-image encryption scheme combining DWT-based compressive sensing with discrete fractional random transform[J]. Opt Commun, 2015, 354:112-121.
    [18]
    Yang B Q, Gu C C, Wu K J, et al. Simultaneous dimensionality reduction and dictionary learning for sparse representation based classification[J]. Multimed Tools Appl, 2017, 76(6):8969-8990.
    [19]
    Mao Y W, Ding F, Liu Y J. Parameter estimation algorithms for hammerstein time-delay systems based on the orthogonal matching pursuit scheme[J]. IET Signal Process, 2017, 11(3):265-274.
    [20]
    Eason D T, Andrews M. Total variation regularization via continuation to recover compressed hyperspectral images[J]. IEEE Trans Image Process, 2015, 24(1):284-293.
    [21]
    Candès E J. The restricted isometry property and its implications for compressed sensing[J]. C R Math, 2008, 346(9-10):589-592.
    [22]
    Candès E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate measurements[J]. Comm Pure Appl Math, 2006, 59(8):1207-1223.
    [23]
    Candès E J, Tao T. Near-optimal signal recovery from random projections:universal encoding strategies?[J]. IEEE Trans Inf Theory, 2006, 52(12):5406-5425.
    [24]
    Sanandaji B M, Vincent T L, Wakin M B. Concentration of measure inequalities for toeplitz matrices with applications[J]. IEEE Trans Signal Process, 2013, 61(1):109-117.
    [25]
    Wang Y L, Yang J F, Yin W T, et al. A new alternating minimization algorithm for total variation image reconstruction[J]. SIAM J Imaging Sci, 2008, 1(3):248-272.
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