Identification, estimation, and control of uncertain dynamic systems : a nonparametric approach
This article is devoted to a presentation of the authors' practice of the non-parametric estimation theory for the estimation, filtering, and control of uncertain dynamic systems. The fundamental advantage of this approach is a weak dependency on prior modeling assumptions about uncertain dynamic components. This approach appears to be of great interest for the control of general discrete-time processes, and in particular, biotechnological processes, which are emblematic of nonlinear uncertain and partially observed systems.
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| Main Authors: | , , , |
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| Format: | article biblioteca |
| Language: | eng |
| Subjects: | U10 - Informatique, mathématiques et statistiques, statistiques, modèle mathématique, biotechnologie, http://aims.fao.org/aos/agrovoc/c_49978, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_16165, |
| Online Access: | http://agritrop.cirad.fr/541924/ http://agritrop.cirad.fr/541924/1/541924.pdf |
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| Summary: | This article is devoted to a presentation of the authors' practice of the non-parametric estimation theory for the estimation, filtering, and control of uncertain dynamic systems. The fundamental advantage of this approach is a weak dependency on prior modeling assumptions about uncertain dynamic components. This approach appears to be of great interest for the control of general discrete-time processes, and in particular, biotechnological processes, which are emblematic of nonlinear uncertain and partially observed systems. |
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