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Volume 1 Issue 4
Oct.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(4): 361-371
Chao Ji, Jing Wang, Liulin Cao and Qibing Jin, "Parameters Tuning of Model Free Adaptive Control Based on Minimum Entropy," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 361-371, 2014.
Citation: Chao Ji, Jing Wang, Liulin Cao and Qibing Jin, "Parameters Tuning of Model Free Adaptive Control Based on Minimum Entropy," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 361-371, 2014.
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Parameters Tuning of Model Free Adaptive Control Based on Minimum Entropy

  • 1. China National Offshore Oil Corporation Research Institute, Beijing 100027, China;

  • 2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Funds:

This work was supported by National Natural Science Foundation of China (61174128, 61473025), Beijing Natural Science Foundation (4132044), and Fundamental Research Funds for the Central Universities of China (YS1404).

    Abstract
  • Dynamic linearization based model free adaptive control (MFAC) algorithm has been widely used in practical systems, in which some parameters should be tuned before it is successfully applied to process industries. Considering the random noise existing in real processes, a parameter tuning method based on minimum entropy optimization is proposed, and the feature of entropy is used to accurately describe the system uncertainty. For cases of Gaussian stochastic noise and non-Gaussian stochastic noise, an entropy recursive optimization algorithm is derived based on approximate model or identified model. The extensive simulation results show the effectiveness of the minimum entropy optimization for the partial form dynamic linearization based MFAC. The parameters tuned by the minimum entropy optimization index shows stronger stability and more robustness than these tuned by other traditional index, such as integral of the squared error (ISE) or integral of timeweighted absolute error (ITAE), when the system stochastic noise exists.

     

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    • References(20)
  • [1]
    Hou Z S, Huang W H. The model-free learning adaptive control of aclass of SISO nonlinear systems. In: Proceedings of the 1997 AmericanControl Conference. New Albuquerque, NM: IEEE, 1997. 343-344
    [2]
    Hou Z S, Jin S T. Model Free Adaptive Control: Theory and Application.Boca Raton: CRC Press, 2013.
    [3]
    Hou Z S, Jin S T. Data driven model-free adaptive control for a classof MIMO nonlinear discrete-time systems. IEEE Transactions on NeuralNetworks, 2011, 22(12): 2173-2188
    [4]
    Xu D Z, Jiang B, Shi P. A novel model free adaptive control designfor multivariable industrial processes. IEEE Transactions on IndustrialElectronics, to be published
    [5]
    Hou Z S, Wang Z. From model-based control to data-driven control:survey, classification and perspective. Information Sciences, 2013, 235:3-35
    [6]
    Wang J, Ji C, Cao L L, Jin Q B. Model free adaptive control and parametertuning based on second order universal model. Journal of CentralSouth University (Science and Technology), 2012, 43(5): 1795-1802
    [7]
    Astrom K J. Introduction to Stochastic Control Theory. New York:Academic, 1970.
    [8]
    Yue H, Wang H. Minimum entropy control of closed-loop trackingerrors for dynamic stochastic systems. IEEE Transactions on AutomaticControl, 2003, 48(1): 118-122
    [9]
    Deniz E, Jose C. An error-entropy minimization algorithm for supervisedtraining of nonlinear adaptive systems. IEEE Transactions on SignalProcessing, 2002, 50(7): 1780-1786
    [10]
    Petersen I R, James M R, Dupuis P. Minimax optimal control ofstochastic uncertain systems with relative entropy constraint. IEEETransactions on Automatic Control, 2000, 45(3): 398-412
    [11]
    Ma Y, Chen X, Xie X H. Research on MFA control algorithm withtracking differentiator. Chinese Journal of Scientific Instrument, 2009,30: 204-208
    [12]
    Chi R H, Hou Z S. A model free periodic adaptive control for freewaytraffic density via ramp metering. Acta Automatica Sinica, 2010, 36(7):1029-1033
    [13]
    Coelho L S, Pessôa M W, Sumar R R. Model free adaptive controldesign using evolutionary-neural compensator. Expert Systems withApplications, 2010, 37(1): 499-508
    [14]
    Yue H, Zhou J L, Wang H. Minimum entropy of B-spline PDF systemswith mean constraint. Automatica, 2006, 42(6): 989-994
    [15]
    Papoulis A, Pillai S U. Probability, Random Variables, and StochasticProcesses. New York: McGraw-Hill, 1991.
    [16]
    Zhuang M, Atherton D P. Tuning PID controllers with integral performancecriteria. In: Proceedings of the 1991 International Conference onControl. Edinburgh: IEEE, 1991. 481-486
    [17]
    Zhuang M, Atherton D P. Automatic tuning of optimum PID controllers.IEE Proceedings D, Control Theory and Applications, 1993, 140(3):216-224
    [18]
    Xu F, Li D H, Xue Y L. Comparing and optimum seeking of PIDtuning methods base on ITAE index. Proceedings of the Csee, 2003,23(8): 206-210
    [19]
    Jin S T, Hou Z S. An improved model-free adaptive control for a classof nonlinear large-lag systems. Control Theory & Applications, 2008,25(4): 623-626
    [20]
    Narendra K S, Parthasarathy K. Identification and control of dynamicalsystems using neural networks. IEEE Transaction on Neural Networks,1990, 1(1): 4-27
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