Advances and selected recent developments in state and parameter estimation |
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| Affiliation: | 1. Department of Chemical Engineering, University of Patras, Karatheodori 1, University Campus, GR 265 00 Patras, Greece;2. Department of Biomedical Engineering and Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA;3. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122, USA;1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea;2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta 30332, GA, USA;3. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, MA, USA;4. Department of Chemical and Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver BC V6T1Z3, Canada;1. Otto-von-Guericke-University Magdeburg, Department Process Systems Engineering, Universitätsplatz 2, D-39106 Magdeburg, Germany;2. Max Planck Institute for Dynamics of Complex Technical Systems, Department Process Systems Engineering, Sandtorstr.1, D-39106 Magdeburg, Germany |
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| Abstract: | This paper deals with two topics from state and parameter estimation. The first contribution of this work provides an overview of techniques used for determining which parameters of a model should be estimated. This is a question that commonly arises when fundamental models are used as these models often contain more parameters than can be reliably estimated from data. The decision of which parameters to estimate is independent of the observer/estimator design, however, it is directly affected by the structure of the model as well as the available data. The second contribution is an overview of recent developments regarding the design of nonlinear Luenberger observers, with special emphasis on exact error linearization techniques, but also discussing more general issues, including observer discretization, sampled data observers and the use of delayed measurements. |
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