Name
Abstract | ||
|---|---|---|
We propose an approach to designing what we call a model-based indirect sensor. This methodology allows estimation of non-measurable variables from indirect measurements, by coupling a dynamical model of the process and an estimation algorithm. This method is especially developed with the aim of allowing a nonspecialist in process control to perform, with limited mathematical development, the state estimation of the processes he has to monitor or control. The main interest of this algorithm is its great simplicity of implementation. Moreover, we present a concept to tune this algorithm based on what we call numerical observability. The performance of this method is illustrated on a real bioprocess. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/87.852906 | Control Systems Technology, IEEE Transactions |
Keywords | Field | DocType |
biotechnology,neural nets,process control,process monitoring,state estimation,bioprocess,dynamical model,estimation algorithm,indirect measurements,model-based indirect sensors,nonspecialist,numerical observability | Observability,Coupling,Control theory,Work in process,Control engineering,Process control,Bioprocess,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 4 | 1063-6536 |
Citations | PageRank | References |
3 | 1.35 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Boillereaux | 1 | 4 | 2.06 |
| Flaus, J.-M. | 2 | 3 | 2.36 |