Batch-to-Batch optimal control based on support vector regression model

  • Authors:

  • Yi Liu;Xianhui Yang;Zhihua Xiong;Jie Zhang

  • Affiliations:

  • Institution of Process Control Engineering, Department of Automation, Tsinghua University, Beijing, China;Institution of Process Control Engineering, Department of Automation, Tsinghua University, Beijing, China;Institution of Process Control Engineering, Department of Automation, Tsinghua University, Beijing, China;Centre for Process Analytics and Control Technology, School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne, UK

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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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.