TY - JOUR
T1 - Nonlinear state and parameter estimation using derivative information
T2 - A Lie-Sobolev approach
AU - Tang, Wentao
AU - Daoutidis, Prodromos
N1 - Funding Information:
This work was supported by National Science Foundation (NSF-CBET) ( CBET-1926303 ).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/8
Y1 - 2021/8
N2 - The implementation of nonlinear control depends on the accuracy of the system model, which, however, is often restricted by parametric and structural uncertainty in the underlying dynamics. In this paper, we propose methods of estimating parameters and states that aim at matching the identified model and the true dynamics not only in the direct output measurements, i.e., in an L2-sense, but also in the higher-order time derivatives of the output signals, i.e., in a Sobolev sense. A Lie-Sobolev gradient descent-based observer-estimator and a Lie-Sobolev moving horizon estimator (MHE) are formulated, and their convergence properties and effects on input–output linearizing control and model predictive control (MPC) respectively are studied. Advantages of Lie-Sobolev state and parameter estimation in nonlinear processes are demonstrated by numerical examples and a reactor with complex dynamics.
AB - The implementation of nonlinear control depends on the accuracy of the system model, which, however, is often restricted by parametric and structural uncertainty in the underlying dynamics. In this paper, we propose methods of estimating parameters and states that aim at matching the identified model and the true dynamics not only in the direct output measurements, i.e., in an L2-sense, but also in the higher-order time derivatives of the output signals, i.e., in a Sobolev sense. A Lie-Sobolev gradient descent-based observer-estimator and a Lie-Sobolev moving horizon estimator (MHE) are formulated, and their convergence properties and effects on input–output linearizing control and model predictive control (MPC) respectively are studied. Advantages of Lie-Sobolev state and parameter estimation in nonlinear processes are demonstrated by numerical examples and a reactor with complex dynamics.
KW - Nonlinear control
KW - Parameter estimation
KW - State estimation
KW - System identification
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U2 - 10.1016/j.compchemeng.2021.107369
DO - 10.1016/j.compchemeng.2021.107369
M3 - Article
AN - SCOPUS:85106655578
SN - 0098-1354
VL - 151
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 107369
ER -