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)|
|Title of host publication||2021 American Control Conference, ACC 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - May 25 2021|
|Event||2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States|
Duration: May 25 2021 → May 28 2021
|Name||Proceedings of the American Control Conference|
|Conference||2021 American Control Conference, ACC 2021|
|City||Virtual, New Orleans|
|Period||5/25/21 → 5/28/21|
Bibliographical notePublisher Copyright:
© 2021 American Automatic Control Council.