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Adaptive linear quadratic gaussian (LQG) control of a bioreactor
Author(s) -
Roux G.,
Dahhou B.,
Najim K.,
Queinnec I.
Publication year - 1992
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.280530205
Subject(s) - control theory (sociology) , linear quadratic gaussian control , linear quadratic regulator , riccati equation , adaptive control , mathematics , covariance , computer science , optimal control , mathematical optimization , differential equation , control (management) , statistics , mathematical analysis , artificial intelligence
The paper deals with the modelling and adaptive control of a continuous‐flow fermentation process for the production of alcohol. The fermenter model has been developed from mass balance and leads to nonlinear differential equations. In practice, control strategies are difficult to derive using this non‐linear model. The dilution rate and the substrate concentration have been considered as control and controlled variables, respectively. The adaptive control algorithm implemented is based on the linear quadratic control approach, where the associated Riccati equation is iterated until the system closed‐loop poles belong to a restricted stability domain which is included in the unit circle. A single input/output model is used for control purposes. The model parameters are estimated on‐line using a robust identification algorithm which includes: data normalization, time‐varying forgetting factor, covariance matrix factorization, etc. Experimental results show the performance of this adaptive scheme and its ability to control biotechnological processes.