Volume 9 Number 6
December 2012
Article Contents
Saïda Bedoui, Majda Ltaief and Kamel Abderrahim. New Results on Discrete-time Delay Systems Identification. International Journal of Automation and Computing, vol. 9, no. 6, pp. 570-577 , 2012. doi: 10.1007/s11633-012-0681-x
Cite as: Saïda Bedoui, Majda Ltaief and Kamel Abderrahim. New Results on Discrete-time Delay Systems Identification. International Journal of Automation and Computing, vol. 9, no. 6, pp. 570-577 , 2012. doi: 10.1007/s11633-012-0681-x

New Results on Discrete-time Delay Systems Identification

  • Received: 2011-04-22
Fund Project:

This work was supported by Ministry of the Higher Education and Scientific Research in Tunisia.

  • A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper. The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector. The gradient algorithm is used to deal with the identification problem. The effectiveness of this method is illustrated through simulation.
  • 加载中
  • [1] C. C. Tsui. Observer design for systems with time-delayedstates. International Journal of Automation and Comput-ing, vol. 9, no. 1, pp. 105-107, 2012.
    [2] Y. Q. Chen, K. L. Moore, J. Yu, T. Zhang. Iterative learningcontrol and repetitive control in hard disk drive industry-a tutorial. International Journal of Adaptive Control andSignal Processing, vol. 22, no. 4, pp. 325-343, 2008.
    [3] J. R. Ryoo, T. Y. Doh. Feedback-based iterative learningcontrol for MIMO LTI systems. International Journal ofControl Automation and Systems, vol. 6, no. 2, pp. 269-277,2008.
    [4] P. Balasubramaniam, T. Senthilkumar. Delay-dependentrobust stabilization and H1 control for uncertain stochas-tic T-S fuzzy systems with discrete interval and distributedtime-varying delays. International Journal of Automationand Computing, vol. 9, no. 3, 2012.
    [5] W. S. Chen, J. M. Li. Adaptive output-feedback regula-tion for nonlinear delayed systems using neural network. In-ternational Journal of Automation and Computing, vol. 5,no. 1, pp. 103-108, 2008,
    [6] T. Söderström, P. Stoica. System Identification, PrenticeHall International, Series in Systems and Control Engineer-ing, New York, USA: Prentice Hall, 1989.
    [7] J. P. Richard. Time-delay systems: An overview of somerecent advances and open problems. Automatica, vol. 39,no. 10, pp. 1667-1694, 2003.
    [8] V. B. Kolmanovskii, S. I. Niculescu, K. Gu. Delay effectson stability: A survey. In Proceedings of the 38th IEEEConference on Decision and Control, IEEE, Phoenix, AZ,USA, vol. 2, pp. 1993-1998, 1999.
    [9] X. M. Ren, A. B. Rad, P. T. Chan, W. L. Lo. Online iden-tification of continuous-time systems with unknown timedelay. IEEE Transactions on Automatic Control, vol. 50,no. 9, pp. 1418-422, 2005.
    [10] S. V. Drakunov, W. Perruquetti, J. P. Richard, L. Belkoura.Delay identification in time-delay systems using variablestructure observers. Annual Reviews in Control, vol. 30,no. 2, pp. 143-158, 2006.
    [11] Y. Orlov, L. Belkoura, J. P. Richard, M. Dambrine. Adap-tive identification of linear time-delay systems. Interna-tional Journal on Robust and Nonlinear Control, vol. 13,no. 9, pp. 857-872, 2003.
    [12] Y. Orlov, L. Belkoura, M. Dambrine, J. P. Richard. Onidentifiability of linear time-delay systems. IEEE Transac-tions on Automatic Control, vol. 47, no. 8, pp. 1319-1324,2002.
    [13] M. de la Sen. Robust adaptive control of linear time-delaysystems with point time-varying delays via multiestimation.Applied Mathematical Modelling, vol. 33, no. 2, pp. 959-977, 2009.
    [14] Q. G. Wang, Y, Zhang. Robust identification of continuoussystems with dead time from step responses. Automatica,vol. 37, no. 3, pp. 377-390, 2001.
    [15] T. Zhang, Y. Q. Cui. A bilateral control of teleopera-tors based on time delay identification. In Proceedings ofthe 2008 IEEE Conference on Robotics, Automation andMechatronics, IEEE, Chengdu, China, pp. 797-802, 2008.
    [16] S. Bedoui, M. Ltaief, K. Abderrahim, R. Ben Abdennour.Representation and control of time delay system: Multi-model approach. In Proceedings of the 8th InternationalMulti-conference on Systems, Signals and Devices, IEEE,Sousse, Tunisia, pp. 1-6, 2011,
    [17] H. Kurz, W. Goedecke. Digital parameter-adaptive controlof process with unknown dead time. Automatica, vol. 17,no. 1, pp. 245-252, 1981.
    [18] P. J. Gawthrop, M. T. Nihtilä. Identification of time de-lays using a polynomial identification method. Systems andControl Letters, vol. 5, no. 4, pp. 267-271, 1985.
    [19] S. W. Sung, I. B. Lee. Prediction error identificationmethod for continuous-time processes with time delay.Industrial and Engineering Chemistry Research, vol. 40,no. 24, pp. 5743-5751, 2001.
    [20] O. Gomez, Y. Orlov, I. V. Kolmanovsky. On-line identifica-tion of SISO linear time-invariant delay systems from out-put measurements. Automatica, vol. 43, no. 12, pp. 2060-2069, 2007.
    [21] S. Ahmed, B. Huang, S. L. Shah. Parameter and delay esti-mation of continuous-time models using a linear filter. Jour-nal of Process Control, vol. 16, no. 4, pp. 323-331, 2006.
    [22] A. B. Rad, W. L. Lo, K. M. Tsang. Simultaneous on-line identification of rational dynamics and time delay: Acorrelation-based approach. IEEE Transactions on ControlSystems Technology, vol. 11, no. 6, pp. 957-959, 2003.
    [23] T. Zhang, Y. C. Li. A fuzzy smith control of time-varyingdelay systems based on time delay identification. In Pro-ceedings of the 2003 International Conference on Ma-chine Learning and Cybernetics, IEEE, Xian, China, vol. 1,pp. 614-619, 2003.
    [24] W. X. Zheng, C. B. Feng. Identification of stochastic timelag systems in the presence of colored noise. Automatica,vol. 26, no. 4, pp. 769-779, 1990.
    [25] W. Gao, Y. C. Li, G. J. Liu, T. Zhang. An adaptive fuzzysmith control of time-varying processes with dominant andvariable delay. In Proceedings of the American Control Con-ference, IEEE, Denver, CO, USA, vol. 1, pp. 220-224, 2003.
    [26] W. Gao, M. L. Zhou, Y. C. Li, T. Zhang. An adaptivegeneralized predictive control of time-varying delay system.In Proceedings of the 2nd International Conference on Ma-chine Learning and Cybernetics, IEEE, Shanghai, China,pp. 878-881, 2004.
    [27] G. Ferretti, C. Maffezzoni, R. Scattolini. Recursive estima-tion of time delay in sampled systems. Automatica, vol. 27,no. 4, pp. 653-661, 1991.
    [28] A. Elnaggar, G. A. Dumont, A. L. Elshafei. New methodfor delay estimation. In Proceedings of the 29th IEEE Con-ference on Decision and Control, IEEE, Honolulu, HI, USA,vol. 3, pp. 1929-1930, 1990.
    [29] L. Xie, Y. J. Liu, H. Z. Yang. Gradient based and leastsquares based iterative algorithms for matrix equationsAXB + CXTD = F. Applied Mathematics and Compu-tation, vol. 217, no. 5, pp. 2191-2199, 2010.
    [30] V. J. Mathews, G. L. Sicuranza. Polynominal Signal Pro-cessing, New York, USA: Wiley, 2000.
    [31] T. Ogunfunmi. Adaptive Nonlinear System Identification:The Volterra and Wiener Model Approaches, New York:Springer, 2007.
    [32] B. Bao, Y. Q. Xu, J. Sheng, R. F. Ding. Least squares basediterative parameter estimation algorithm for multivariablecontrolled ARMA system modelling with finite measure-ment data. Mathematical and Computer Modelling, vol. 53,no. 9-10, pp. 1664-1669, 2011.
    [33] D. Q. Wang, F. Ding. Least squares based and gradientbased iterative identification for Wiener nonlinear systems.Signal Processing, vol. 91, no. 5, pp. 1182-1189, 2011.
    [34] D. Q. Wang, F. Ding. Input-output data filtering based re-cursive least squares identification for CARARMA systems.Digital Signal Processing, vol. 20, no. 4, pp. 991-999, 2010.
    [35] O. Nelles. Nonlinear System Identification: From Classi-cal Approach to Neural Networks and Fuzzy Models, NewYork, USA: Springer, 2001.
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New Results on Discrete-time Delay Systems Identification

Fund Project:

This work was supported by Ministry of the Higher Education and Scientific Research in Tunisia.

Abstract: A new approach for simultaneous online identification of unknown time delay and dynamic parameters of discrete-time delay systems is proposed in this paper. The proposed algorithm involves constructing a new generalized regression vector and defining the time delay and the rational dynamic parameters in the same vector. The gradient algorithm is used to deal with the identification problem. The effectiveness of this method is illustrated through simulation.

Saïda Bedoui, Majda Ltaief and Kamel Abderrahim. New Results on Discrete-time Delay Systems Identification. International Journal of Automation and Computing, vol. 9, no. 6, pp. 570-577 , 2012. doi: 10.1007/s11633-012-0681-x
Citation: Saïda Bedoui, Majda Ltaief and Kamel Abderrahim. New Results on Discrete-time Delay Systems Identification. International Journal of Automation and Computing, vol. 9, no. 6, pp. 570-577 , 2012. doi: 10.1007/s11633-012-0681-x
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