Adaptive regulation of blood glucose levels: A triadic methodology incorporating super twisting and deep policy gradient

•Developing a Super-Twisting Sliding Mode Control (SSMC) for blood glucose regulation.•Integrating a Deep Reinforcement Learning (DRL) algorithm to optimize SSMC gains.•Using random network distillation (RND) in DRL for improved exploration and learning.•Implementing a Sine-Cosine Algorithm (SCA) to...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Biomedical signal processing and control Jg. 103; S. 107444
Hauptverfasser:	Mohammadzadeh, Ardashir, Alattas, Khalid A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.05.2025
Schlagworte:	Closed-loop controller Deep reinforcement learning algorithm (DRL) Diabetes patients Pancreas system Sine-Cosine algorithm (SCA) Supertwisting sliding mode control (SSMC) Pancreas system Deep reinforcement learning algorithm (DRL) Sine-Cosine algorithm (SCA) Closed-loop controller Diabetes patients Supertwisting sliding mode control (SSMC)
ISSN:	1746-8094
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	•Developing a Super-Twisting Sliding Mode Control (SSMC) for blood glucose regulation.•Integrating a Deep Reinforcement Learning (DRL) algorithm to optimize SSMC gains.•Using random network distillation (RND) in DRL for improved exploration and learning.•Implementing a Sine-Cosine Algorithm (SCA) to tune hyperparameters in DRL.•Testing the SSMC-based DRL-SCA system on six diverse diabetic patient profiles. The insufficiency of insulin secretion by the pancreas leads to elevated blood glucose levels (BGL) in individuals diagnosed with diabetes. Addressing this challenge, the artificial pancreas (AP) has emerged as a viable solution for the autonomous regulation of BGL through the continuous infusion of insulin. The proposed methodology employs a triadic approach to develop an intelligent closed-loop pancreatic system. First, a nonlinear controller, specifically the supertwisting sliding mode control (SSMC), is applied for the regulation of blood glucose. To enhance the quality and efficacy of the controller, a deep reinforcement learning (DRL) algorithm is then integrated to facilitate online updates of the SSMC coefficients. Finally, a sine–cosine algorithm (SCA) is formulated to optimize the hyperparameters of the DRL approach, addressing the challenges associated with the calibration of reinforcement learning algorithms. The developed system has been evaluated on six diabetic patients, involving various tests with differing BGL references, meal consumption, and the management of uncertainties. A comparative analysis with conventional methodologies further substantiates the superiority of the proposed SSMC-based DRL-SCA system, demonstrating an overall average superiority of approximately 61.13% in tracking performance compared to traditional control methods across various cases.
ISSN:	1746-8094
DOI:	10.1016/j.bspc.2024.107444