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    Comparison of control algorithms for the blood glucose concentration in a virtual patient with an artificial pancreas
    (Elsevier, 2012) Semizer, E.; Yuceer, M.; Atasoy, I.; Berber, R.
    To obtain the most suitable control algorithm for a wearable artificial pancreas, different control algorithms were compared and tested using a Hovorka model. Model predictive control (MPC), linear and nonlinear model forms, proportional integral derivative control (PID), neural-network-based model predictive control (NN-MPC), nonlinear autoregressive moving average (NARMA-L2) and sequential quadratic programming (SQP) were evaluated using the Hovorka model. Due to the fact that modeling of biomedical processes are very complex, to present the most effective control algorithm, various control strategies were needed to application. In the control algorithms, set point tracking and disturbance rejection were performed. With respect to the rise times of the control algorithms, SQP with optimal control had the shortest time, and NARMA-L2 had the longest time. Because the control algorithm connects the glucose meter and the insulin pump in an artificial pancreas, the rise time is the most important parameter. We propose that optimal control with SQP is the most suitable control algorithm to connect the glucose meter and the insulin pump. (C) 2011 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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    A SOFTWARE FOR PARAMETER ESTIMATION IN DYNAMIC MODELS
    (Brazilian Soc Chemical Eng, 2008) Yuceer, M.; Atasoy, I.; Berber, R.
    A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.