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Design of an online-tuned model based compound controller for a fully automated artificial pancreas

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Abstract

This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients’ intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia.

A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.

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Notes

  1. This paper is a full version of the abstract that appeared in ATTD 2017 [29].

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Acknowledgements

The authors wish to thank their funding source, NTU-NHG Ageing Research Grant: ARG/14015.

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Authors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Arpita Bhattacharjee, Arvind Easwaran, Melvin Khee-Shing Leow & Namjoon Cho

  2. Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore

    Melvin Khee-Shing Leow

  3. Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore

    Melvin Khee-Shing Leow

  4. Office of Clinical Sciences, Duke-NUS Graduate Medical School, Singapore, Singapore

    Melvin Khee-Shing Leow

  5. Lee Kong Chian School of Medicine-Imperial College London, London, SW7 2DD, UK

    Melvin Khee-Shing Leow

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  1. Arpita Bhattacharjee
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  3. Melvin Khee-Shing Leow
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  4. Namjoon Cho
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Correspondence to Arpita Bhattacharjee.

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Bhattacharjee, A., Easwaran, A., Leow, M.KS. et al. Design of an online-tuned model based compound controller for a fully automated artificial pancreas. Med Biol Eng Comput 57, 1437–1449 (2019). https://doi.org/10.1007/s11517-019-01972-5

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