NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor<sup>*</sup>

冯絮影; 于涛; 王建林

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NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor^*

Updated：2026-01-06

- NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor^*
- NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor^*
- 中国化学工程学报（英文版） 2012年20卷第5期页码：988-994
- Affiliations：
  
  College of Information Science and Technology, Beijing University of Chemical Technology,Beijing,China,100029
- Author bio：
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- DOI：
  中图分类号：
- 纸质出版：2012
- Accepted：
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冯絮影, 于涛, 王建林. NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor^*[J]. 中国化学工程学报（英文版）, 2012,20(5):988-994.

[J]. Chinese Journal of Chemical Engineering, 2012, 20(5): 988-994.
冯絮影, 于涛, 王建林. NonliNonlinear GPC with In-place Trained RLS-SVM Model for DOC Control in a Fed-batch Bioreactor^*[J]. 中国化学工程学报（英文版）, 2012,20(5):988-994. DOI：

[J]. Chinese Journal of Chemical Engineering, 2012, 20(5): 988-994. DOI：

摘要

Abstract

In this study

Saccharomyces cerevisiae

(baker's yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However

it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine

which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process

and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control

experimental results demonstrate the robustness

effectiveness and advantages of the new controller.

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