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Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints

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Abstract

This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm (MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.

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Authors and Affiliations

  1. Department of Automation, Beijing University of Chemical Technology, Beijing, 100029, China

    Da-zi Li  (李大字), Yuan-xin Jia  (贾元昕) & Qi-bing Jin  (靳其兵)

  2. Beijing Century Robust Technology Co. Ltd., Beijing, 100020, China

    Quan-shan Li  (李全善)

Authors
  1. Da-zi Li  (李大字)
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  2. Yuan-xin Jia  (贾元昕)
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  3. Quan-shan Li  (李全善)
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  4. Qi-bing Jin  (靳其兵)
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Corresponding author

Correspondence to Da-zi Li  (李大字).

Additional information

Foundation item: Projects(61573052, 61273132) supported by the National Natural Science Foundation of China

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Li, Dz., Jia, Yx., Li, Qs. et al. Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints. J. Cent. South Univ. 24, 448–458 (2017). https://doi.org/10.1007/s11771-017-3447-3

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  • DOI: https://doi.org/10.1007/s11771-017-3447-3

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