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Batch-to-Batch Optimal Control Based on Support Vector Regression Model

  • Yi Liu
  • Xianhui Yang
  • Zhihua Xiong
  • Jie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

A support vector regression (SVR) model based batch to batch optimal control strategy is proposed in this paper. Because of model plant mismatches and unknown disturbances the control performance of optimal control profile calculated from empirical model is deteriorated. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch to batch optimal control strategy based on the linearization of the SVR model around the control profile is proposed in this paper. Applications to a simulated batch styrene polymerization reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.

Keywords

Support Vector Regression Batch Process Iterative Learn Control Support Vector Regression Model Control Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Benson, R.: Process Control – the Future. Computing and Control Engineering Journal 8, 161–166 (1997)CrossRefGoogle Scholar
  2. 2.
    Flores-Cerrillo, J., MacGregor, J.F.: Within-Batch and Batch-to-Batch Inferential- Adaptive Control of Semibatch Reactors: A Partial Least Squares Approach. Ind. Eng. Chem. Res. 42, 3334–3345 (2003)CrossRefGoogle Scholar
  3. 3.
    Xiong, Z.H., Zhang, J.: Product Quality Trajectory Tracking in Batch Processes Using Iterative Learning Control Based on Time-Varying Perturbation Models. Ind. Eng. Chem. Res. 42, 6802–6814 (2003)CrossRefGoogle Scholar
  4. 4.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New-York (1995)zbMATHGoogle Scholar
  5. 5.
    Smola, A., Scholkopf, B.: A Tutorial on Support Vector Regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 (1998)Google Scholar
  6. 6.
    Suykens, J.A.K., Vandewalle, J., De Moor, B.: Optimal Control by Least Squares Support Vector Machines. Neural Networks 14, 23–35 (2001)CrossRefGoogle Scholar
  7. 7.
    Zhang, J.: Neural Network Model Based Batch-to-Batch Optimal Control. In: Proceedings of the 2003 IEEE International Symposium on Intelligent Control, Houston, Texas (2003)Google Scholar
  8. 8.
    Kwon, Y.D., Evans, L.B.: A Coordinate Transformation Method for the Numerical Solution of Nonlinear Minimum-Time Control Problems. AIChE J. 21, 1158–1164 (1975)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yi Liu
    • 1
  • Xianhui Yang
    • 1
  • Zhihua Xiong
    • 1
  • Jie Zhang
    • 2
  1. 1.Institution of Process Control Engineering, Department of AutomationTsinghua UniversityBeijingChina
  2. 2.Centre for Process Analytics and Control Technology, School of Chemical Engineering and Advanced MaterialsUniversity of NewcastleNewcastle upon TyneUK

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