Structural Conditions on Observability of Nonlinear Systems

Ma, Qiang

doi:10.5815/ijitcs.2011.04.03

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The radome aberration angle is modelled with radome slope, and it is considered as a state variable. The local observability analysis method for nonlinear dynamic system based on Lie derivative and gradient operator, introduced by Hwang (1972), Bartosiewicz (1995), and Ma (2011), is utilized to derive the observable condition of the target state variables and radome aberration estimation system. This paper is composed as follows.…”

Section: Introductionmentioning

confidence: 99%

Observability Analysis on Radome Aberration Estimation

Seo

Tahk

Ryoo

2014

IFAC Proceedings Volumes

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Section: Introductionmentioning

confidence: 99%

Observability Analysis on Radome Aberration Estimation

Seo

Tahk

Ryoo

2014

IFAC Proceedings Volumes

View full text Add to dashboard Cite

Multivariable identification based MPC for closed-loop glucose regulation subject to individual variability

Wang,

Zhang

et al. 2023

Computer Methods in Biomechanics and Biomedical Engineering

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An OGI model for personalized estimation of glucose and insulin concentration in plasma

Wang

Geng

et al. 2021

MBE

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<abstract><p>Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC $ 9.49\pm3.81 $ mU/L, and PGC $ 0.89\pm0.19 $ mmol/L. For human, the OGI with PFM has the promise to identify disturbances ($ 95.46\%\pm0.65\% $ accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.</p></abstract>

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Structural Conditions on Observability of Nonlinear Systems

Cited by 3 publications

References 13 publications

Observability Analysis on Radome Aberration Estimation

Observability Analysis on Radome Aberration Estimation

Multivariable identification based MPC for closed-loop glucose regulation subject to individual variability

An OGI model for personalized estimation of glucose and insulin concentration in plasma

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