Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems

Hajizadeh, Iman; Rashid, Mudassir; Turksoy, Kamuran; Samadi, Sediqeh; Feng, Jun‐e; Sevil, Mert; Frantz, Nicole; Lazaro, C. Burgos; Maloney, Zacharie; Littlejohn, Elizabeth; Çınar, Ali

doi:10.1016/j.ifacol.2017.08.268

Cited by 20 publications

(13 citation statements)

References 18 publications

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“…Blood glucose-insulin models can be physiological or datadriven [39,40]. The former are usually based on a compartmental representation [41,24,42], while the latter ones normally rely on time-series approaches [43,44]. Although both kinds of approaches have been used for control-oriented models [45,44,43], physiological-based models are preferable due to their inherent descriptive ability and parameter interpretability.…”

Section: Blood Glucose-insulin Modelmentioning

confidence: 99%

“…The former are usually based on a compartmental representation [41,24,42], while the latter ones normally rely on time-series approaches [43,44]. Although both kinds of approaches have been used for control-oriented models [45,44,43], physiological-based models are preferable due to their inherent descriptive ability and parameter interpretability. In this work a physiological long-term minimal model based on Ruan proposal [34] is selected as the control-oriented model to be used for both, the state estimator and the pZMPC.…”

Section: Blood Glucose-insulin Modelmentioning

confidence: 99%

See 1 more Smart Citation

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

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Section: Blood Glucose-insulin Modelmentioning

confidence: 99%

Section: Blood Glucose-insulin Modelmentioning

confidence: 99%

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

View full text Add to dashboard Cite

“…The recursive PBSID approach is the identification technique used to track the time-varying system by adapting a linear model [10][11][12][13]. In this modeling approach, the stability and fidelity of recursively identified models are ensured by considering constraints for the stability and the correctness of the gain between inputs and output.…”

Section: Appendix a -: The Glucose-insulin Dynamic Model Of Hovorkamentioning

confidence: 99%

“…The PBSID approach is extended to provide stable, time-varying individualized state-space models for glycemic predictions using estimates of the PIC. The adaptive identification allows the realized models to be valid over a diverse range of daily conditions without requiring obscure information on meals, thus avoiding manual meal announcement entries by users [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

Hajizadeh

Rashid

Çınar

2019

Journal of Process Control

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An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closedloop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

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“…In one direction, Dynamic Mode Decomposition with control (DMDc) has been utilized to extract low-order models from high-dimensional, complex systems [10,11]. In another direction, subspace-based system identification methods have been adapted for the purpose of model identification, where state-space model from measured data are identified using projection methods [12][13][14]. To handle the resultant plant model mismatch with data-driven model based approaches, monitoring of the model validity becomes especially important.…”

Section: Introductionmentioning

confidence: 99%

Data Driven Economic Model Predictive Control

Kheradmandi

Mhaskar

2018

Mathematics

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This manuscript addresses the problem of data driven model based economic model predictive control (MPC) design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example.

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Multivariable Recursive Subspace Identification with Application to Artificial Pancreas Systems

Cited by 20 publications

References 18 publications

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

Data Driven Economic Model Predictive Control

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