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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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