Confidence bounds on identification of linear systems with multiplicative noise
B Di, A Lamperski - 2021 American Control Conference (ACC), 2021 - ieeexplore.ieee.org
B Di, A Lamperski
2021 American Control Conference (ACC), 2021•ieeexplore.ieee.orgLinear 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 …
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 …
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.
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