SCI和EI收录∣中国化工学会会刊

Chin.J.Chem.Eng. ›› 2016, Vol. 24 ›› Issue (5): 623-629.DOI: 10.1016/j.cjche.2015.12.011

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Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

Xuan Yang, Xiao'e Ruan   

  1. Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
  • Received:2015-02-12 Revised:2015-07-27 Online:2016-06-14 Published:2016-05-28
  • Contact: Xiao'e Ruan
  • Supported by:

    Supported by National Natural Science Foundation of China (F010114-60974140, 61273135).

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

Xuan Yang, Xiao'e Ruan   

  1. Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
  • 通讯作者: Xiao'e Ruan
  • 基金资助:

    Supported by National Natural Science Foundation of China (F010114-60974140, 61273135).

Abstract: In this paper, a reinforced gradient-type iterative learning control profile is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise. The robustness is analyzed and the range of the step is specified by means of statistical technique and matrix theory. Compared with the conventional one, the proposed algorithm is more efficient to resist external noise. Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

Key words: Batch process, Iterative learning control, Reinforced gradient, Gaussian white noise

摘要: In this paper, a reinforced gradient-type iterative learning control profile is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise. The robustness is analyzed and the range of the step is specified by means of statistical technique and matrix theory. Compared with the conventional one, the proposed algorithm is more efficient to resist external noise. Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

关键词: Batch process, Iterative learning control, Reinforced gradient, Gaussian white noise