20,266 research outputs found
Optimal Regulation of Blood Glucose Level in Type I Diabetes using Insulin and Glucagon
The Glucose-Insulin-Glucagon nonlinear model [1-4] accurately describes how
the body responds to exogenously supplied insulin and glucagon in patients
affected by Type I diabetes. Based on this model, we design infusion rates of
either insulin (monotherapy) or insulin and glucagon (dual therapy) that can
optimally maintain the blood glucose level within desired limits after
consumption of a meal and prevent the onset of both hypoglycemia and
hyperglycemia. This problem is formulated as a nonlinear optimal control
problem, which we solve using the numerical optimal control package PSOPT.
Interestingly, in the case of monotherapy, we find the optimal solution is
close to the standard method of insulin based glucose regulation, which is to
assume a variable amount of insulin half an hour before each meal. We also find
that the optimal dual therapy (that uses both insulin and glucagon) is better
able to regulate glucose as compared to using insulin alone. We also propose an
ad-hoc rule for both the dosage and the time of delivery of insulin and
glucagon.Comment: Accepted for publication in PLOS ON
Depth of anesthesia control using internal model control techniques
The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These
discrepancies are translated into the pharmacokinetics (PK),
and pharmacodynamics (PD). These uncertainties may affect
the stability of the closed loop control system. This paper aims at developing predictive controllers using Internal Model Control technique. This study develops patient dose-response models and to provide an adequate drug administration regimen for the anaesthesia to avoid under or over dosing of the patients. The controllers are designed to compensate for patients inherent drug response variability, to achieve the best output disturbance rejection, and to maintain optimal set point response. The results are evaluated compared with traditional PID controller and the performance is confirmed in our
simulation
Stabilising Model Predictive Control for Discrete-time Fractional-order Systems
In this paper we propose a model predictive control scheme for constrained
fractional-order discrete-time systems. We prove that all constraints are
satisfied at all time instants and we prescribe conditions for the origin to be
an asymptotically stable equilibrium point of the controlled system. We employ
a finite-dimensional approximation of the original infinite-dimensional
dynamics for which the approximation error can become arbitrarily small. We use
the approximate dynamics to design a tube-based model predictive controller
which steers the system state to a neighbourhood of the origin of controlled
size. We finally derive stability conditions for the MPC-controlled system
which are computationally tractable and account for the infinite dimensional
nature of the fractional-order system and the state and input constraints. The
proposed control methodology guarantees asymptotic stability of the
discrete-time fractional order system, satisfaction of the prescribed
constraints and recursive feasibility
- …