Recursive prediction error methods for online estimation in nonlinear state-space models
Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Norwegian Society of Automatic Control
1994-04-01
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| Series: | Modeling, Identification and Control |
| Subjects: | |
| Online Access: | http://www.mic-journal.no/PDF/1994/MIC-1994-2-4.pdf |
| Summary: | Several recursive algorithms for online, combined state and parameter estimation in nonlinear state-space models are discussed in this paper. Well-known algorithms such as the extended Kalman filter and alternative formulations of the recursive prediction error method are included, as well as a new method based on a line-search strategy. A comparison of the algorithms illustrates that they are very similar although the differences can be important for the online tracking capabilities and robustness. Simulation experiments on a simple nonlinear process show that the performance under certain conditions can be improved by including a line-search strategy. |
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| ISSN: | 0332-7353 1890-1328 |