Abstract
Linear systems with multiplicative noise (LSMN) generalize the more common case of additive noise models. The multiplicative noise can model state-dependent noise and variations in the dynamics. We present an LSMN system identification algorithm which estimates both the first and second moments of the system parameters, and offers a probability bound on the estimates. We further develop an online scheme for identification and a robust control scheme based on the estimation bounds. Numerical examples are provided.
| Original language | English (US) |
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| Title of host publication | 2021 American Control Conference, ACC 2021 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2212-2217 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665441971 |
| DOIs | |
| State | Published - May 25 2021 |
| Event | 2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States Duration: May 25 2021 → May 28 2021 |
Publication series
| Name | Proceedings of the American Control Conference |
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| Volume | 2021-May |
| ISSN (Print) | 0743-1619 |
Conference
| Conference | 2021 American Control Conference, ACC 2021 |
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| Country/Territory | United States |
| City | Virtual, New Orleans |
| Period | 5/25/21 → 5/28/21 |
Bibliographical note
Publisher Copyright:© 2021 American Automatic Control Council.