Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control

Publication date: March 2018 Source:Biomedical Signal Processing and Control, Volume 41 Author(s): Arpita Bhattacharjee, Arvind Easwaran, Melvin Khee-Shing Leow, Namjoon Cho A fully-automated controller in the artificial pancreas (AP) system designed to regulate blood glucose concentration can give better lifestyle to a type 1 diabetic patient. This paper deals with evaluating the benefit of fully-automated online-tuned controller for the AP system over offline-tuned and semi-automated controller based on internal model control (IMC) strategy. The online-tuned controller is fully-automatic in the sense that it can automatically deal with intra- and inter-patient variabilities and compensate for unannounced meal disturbances without any prior knowledge of patient parameters, patient specific characteristics or patient specific input–output data. A data driven Volterra model of patients is used to design IMC algorithms. For online-tuned controller, the Volterra kernels of the model are computed online by recursive least squares algorithm. The IMC algorithms are evaluated using different scenarios in the UVA/Padova metabolic simulator for validation, comparison with a fully-automatic zone model predictive controller and robustness analysis. Unlike offline-tuned IMC and semi-automated IMC, the online-tuned IMC in the AP system performs satisfactorily for every patient condition without patients’ intervention. Experimental results show that the online-tuned IMC compensat...
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research