ISBN: 978-981-14-4787-7 DOI: 10.18178/wcse.2020.06.049
In-silico Testing of a Generalized Predictive Control Based Artificial Pancreas for Child Patients with Type 1 Diabetes
Abstract— We have developed a generalized predictive control (GPC) based controller with two adaptive strategies, namely, an adaptive reference glucose trajectory (ARGT) and an adaptive softening factor (ASF), for artificial pancreas systems (AP). Tests with the UVA/Padova type 1 diabetes mellitus simulator (T1DMS), approved by the US Food and Drug Administration, showed that it realized the effective control of the blood glucose concentrations (BGCs) of adult and adolescent patients. Here, the GPC controller and those two adaptive strategies (ARGT and ASF) were further tested with 10 in-silico children by using the UVA/Padova T1DMS. Results showed that ARGT and ASF strategies significantly increased the quality of the GPC controller and the BGCs of 10 child patients were effectively controlled with the GPC+ARGT+ASF controller.
Index Terms—generalized predictive control, artificial pancreas, children, adaptive softening factor, adaptive reference glucose trajectory
Wenping Liu, Ting Chen, Liling Yu, Haoyu Jin
Institute of Medical Devices, Guangdong Food and Drug Vocational College, CHINA
Cite:Wenping Liu, Ting Chen, Liling Yu, Haoyu Jin , " In-silico Testing of a Generalized Predictive Control Based Artificial Pancreas for Child Patients with Type 1 Diabetes " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 330-335, Shanghai, China, 19-21 June, 2020.