Approximative model reconstruction of cascade systems |
| |
| Affiliation: | 1. Department of Allergy, Research Institute Hospital 12 de Octubre, Avenida de Córdoba s/n, 28041 Madrid, Spain;2. Departamento de Bioquímica y Biología Molecular, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Madrid, Spain;3. Institute of Sciences of Food Production, CNR-ISPA, 70126 Bari, Italy;4. Department of Food Technology, National Institute of Agricultural, Food Research, and Technology (INIA), Ctra. La Coruña Km. 7.5, 28040 Madrid, Spain;5. Department of Dermatology and Allergy, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany;1. Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore;2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore;3. Department of Aeronautics, Imperial College London, United Kingdom;4. Department of Electrical and Electronic Engineering, University of Melbourne, Australia;1. Department of Earth and Planetary Sciences, Rutgers University, 610 Taylor Road, Piscataway, NJ 08854, USA;2. Institute of Earth, Ocean and Atmospheric Sciences, Rutgers University, 71 Dudley Road, New Brunswick, NJ 08901, USA;3. Rutgers Energy Institute, Rutgers University, 71 Dudley Road, New Brunswick, NJ 08901, USA;4. Sea Level Research, Department of Marine and Coastal Sciences, Rutgers University, 71 Dudley Road, New Brunswick, NJ 08901, USA;5. Earth Observatory of Singapore, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;6. Asian School of the Environment, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;7. New Jersey Geological and Water Survey, P.O. Box 420, Trenton, NJ 08625-0420, USA;1. Hatfield Research Laboratories, Eisai Ltd., Hatfield AL10 9SN, United Kingdom;2. Charles River Laboratories, 7-9 Spire Green Centre, Flex Meadow, Harlow, Essex CM19 5TR, United Kingdom;3. Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom |
| |
| Abstract: | This letter considers how to approximately reconstruct a cascade system from a given unstructured system estimate. Many system identification methods, including subspace methods, provide reliable but generally unstructured black-box models. The problem we consider is how to find cascade systems that are close to such black-box models. For this, we use model matching techniques and optimal weighted Hankel-norm approximation to obtain accurate low-order cascade systems. We show that it is possible to bound the reconstruction error in terms of an error tolerance parameter and weighted Hankel singular values. The suggested methods are illustrated on both a numerical example and a real double tank system with experimental data. |
| |
| Keywords: | Model reduction Cascade systems Model matching |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
