In this paper, we present two simple and novel methods for automatic personalization of target blood glucose concentration values for individuals with Type 2 Diabetes (T2D). The methods can be integrated with any insu...
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This paper presents an extension of the so-called 'Hansen scheme' for turning closed-loop system identification into open-loop-like identification to a class of discrete-time nonlinear systems with sector-boun...
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The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to find ground states of Hamiltonians and solve quadratic unconstrained binary optimization problems. This paper efficiently ge...
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Estimation of states in stochastic differential equations with state dependent diffusion is known to be difficult. Previous research recommend the higher order extended Kalman filter or the Lamperti transform method f...
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Estimation of states in stochastic differential equations with state dependent diffusion is known to be difficult. Previous research recommend the higher order extended Kalman filter or the Lamperti transform method for this case. This paper shows that a new developed method, based on the unscented Kalman filter, is superior for two simulated stochastic differential equation systems.
This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking pe...
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This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.
Flow variations of the inlet to a wastewater treatment plant (WWTP) are problematic due to the biological purification process. A way to reduce variations from industrial areas is to insert a buffer tank and control t...
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All flow systems are subject to transport delays, which are determined by the flow rates in the system. When the flow rates themselves are control inputs, the system becomes subject to input-dependent state delays , w...
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All flow systems are subject to transport delays, which are determined by the flow rates in the system. When the flow rates themselves are control inputs, the system becomes subject to input-dependent state delays , which poses significant theoretical problems. In this paper we propose a model predictive control scheme for a generic multi-variable heat transport system, where flows to the individual consumers can be manipulated by a centralized controller. The control design takes the transport delay into account by means of an explicit discretization of the transport equation, which is a partial differential equation. Different discretization methods are considered, giving rise to a high-order bilinear system model in discrete time. The control problem is formulated as a linear quadratic receding horizon problem with terminal cost. Simulation studies show that the control problem can be solved using standard software and that the Lax-Friedrichs discretization method appears to be the most suitable of the investigated methods.
This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose meas...
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This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D.
In this paper, we present two simple and novel methods for automatic personalization of target blood glucose concentration values for individuals with Type 2 Diabetes (T2D). The methods can be integrated with any insu...
In this paper, we present two simple and novel methods for automatic personalization of target blood glucose concentration values for individuals with Type 2 Diabetes (T2D). The methods can be integrated with any insulin dosing algorithm, or used to provide an individualized reference BG concentration value for medical professionals to consider when determining long-acting insulin doses and other oral medications. The proposed methods were tested in three different simulation models, with different long-acting insulin dosing strategies, and were found to reduce instances of hypoglycemia.
This paper presents an extension of the so-called “Hansen scheme” for turning closed-loop system identification into open-loop-like identification to a class of discrete-time nonlinear systems with sector-bounded no...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper presents an extension of the so-called “Hansen scheme” for turning closed-loop system identification into open-loop-like identification to a class of discrete-time nonlinear systems with sector-bounded nonlinearities in the state equation. In order to deploy the Hansen scheme, it is necessary to know the existence of a dual Youla-Kucera parametrization of all plants controlled by an observer-based controller. We deduce the existence of such a parametrization based on the solution of a pair of Linear Matrix Inequalities, combined with some differential boundedness arguments. The dual Youla-Kucera parameter may be identified in a number of different ways; in the paper, two examples are presented.
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