A Feedback-based Optimization Method for Uncertain Batch Processes

DOI

10.2991/ecae-17.2018.68How to use a DOI?

Keywords

batch process; dynamic optimization; output feedback; uncertainty

Abstract

Optimal control of batch processes is often implemented in an open-loop manner with an online optimizer. In this paper, a feedback-based optimization method is proposed for batch processes which suffer from parametric uncertainties. Firstly, the optimality conditions and the expression for optimal input are derived based on the uncertain parametric model, then the output measurements and corresponding optimal inputs are collected via off-line simulation. Then, the explicit control law is obtained through regression, which is implemented in a feedback manner for optimization purpose. Case study on a fed-batch reactor indicates that the proposed approach can attain good optimizing performance in a wide range of uncertainties.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jing Zhu
AU  - Lingjian Ye
AU  - Wanqing Tao
AU  - Xiushui Ma
PY  - 2017/12
DA  - 2017/12
TI  - A Feedback-based Optimization Method for Uncertain Batch Processes
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Control and Automation Engineering (ECAE 2017)
PB  - Atlantis Press
SP  - 315
EP  - 319
SN  - 2352-5401
UR  - https://doi.org/10.2991/ecae-17.2018.68
DO  - 10.2991/ecae-17.2018.68
ID  - Zhu2017/12
ER  -