Recursive State-space Model Identification of Non-uniformly Sampled Systems Using Singular Value Decomposition |
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| Affiliation: | 1. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada;2. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada;3. School of Engineering, Robert Gordon University, Aberdeen, United Kingdom;1. Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Dr., Cambridge CB3 0AS, United Kingdom;2. Pharmaceutical Technology & Development, AstraZeneca, Charter Way, Macclesfield SK10 2NA, United Kingdom;3. School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore;1. Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Dr, Cambridge CB3 0AS, United Kingdom;2. Pharmaceutical Technology & Development, AstraZeneca, Charter Way, Macclesfield SK10 2NA, United Kingdom;3. Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstraße 39, Berlin 10117, Germany;4. School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, Singapore 637459, Singapore |
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| Abstract: | In this paper a recursive state-space model identification method is proposed for non-uniformly sampled systems in industrial applications. Two cases for measuring all states and only output(s) of such a system are considered for identification. In the case of state measurement, an identification algorithm based on the singular value decomposition (SVD) is developed to estimate the model parameter matrices by using the least-squares fitting. In the case of output measurement only, another identification algorithm is given by combining the SVD approach with a hierarchical identification strategy. An example is used to demonstrate the effectiveness of the proposed identification method. |
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