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Optimization Techniques for Online MPC in Android Smartphones for Artificial Pancreas: A Comparison Study

Chandrasekhar, A and Padhi, R (2022) Optimization Techniques for Online MPC in Android Smartphones for Artificial Pancreas: A Comparison Study. In: 7th International Conference on Advances in Control and Optimization of Dynamical Systems, ACODS 2022, 22 - 25 February 2022, Silchar, Assam, pp. 561-566.

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Official URL: https://doi.org/10.1016/j.ifacol.2022.04.092


Model Predictive Control (MPC) has evolved as a potential approach for safety-critical Artificial Pancreas (AP) systems because of its constraint handling capability. However, it demands a solution of a fairly high-dimensional constrained optimization problem online. A popular approach to close the loop in practice is to implement it in smartphones in general and in Android smartphones in particular (because of its pervasiveness). As MPC requires the use of Optimization solvers in smartphones, a survey of various available options has been done in this paper, followed by a thorough comparison study about their performance. This comprehensive comparison includes the ease of convergence of the optimization solution and the computational time. It turns out that the Python-based optimization solver SciPy is most suitable for this purpose as compared to other alternatives such as Casadi, lpsolvers and autocode generation process of MATLAB. The outcome of this study can be used for rapid prototyping of Android smartphone-based AP systems.

Item Type: Conference Paper
Publication: IFAC-PapersOnLine
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Artificial Pancreas (AP); Embedded Control; Model Predictive Control (MPC); Online Optimization; Optimization; Smartphone Application
Department/Centre: Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Date Deposited: 05 Jul 2022 11:22
Last Modified: 05 Jul 2022 11:22
URI: https://eprints.iisc.ac.in/id/eprint/74142

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