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Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

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  • Dazi Li
  • Qianwen Xie
  • Qibing Jin

Abstract

A new strategy for internal model control (IMC) is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM). Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS), while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

Suggested Citation

  • Dazi Li & Qianwen Xie & Qibing Jin, 2015. "Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, May.
    • Handle: RePEc:hin:jnlmpe:181389
    DOI: 10.1155/2015/181389
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