Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System

Yan, Shu‐Rong; Alattas, Khalid A.; Bakouri, Mohsen; Alanazi, Abdullah K.; Mohammadzadeh, Ardashir; Mobayen, Saleh; Zhilenkov, Anton; Guo, Wei

doi:10.3390/math10050690

Cited by 10 publications

(9 citation statements)

References 39 publications

(42 reference statements)

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“…𝑋(𝑖𝑡𝑒𝑟 + 1) = |𝑋 𝑝 (𝑖𝑡𝑒𝑟) − 𝐴 × 𝐷| (10) where the distance between the grey wolf individual and its prey is represented by Eq. ( 9).…”

Section: The Gwo Algorithmmentioning

confidence: 99%

“…Obtain the best gain control parameters of the PID controller position update formula is found in Eq. (10), where iter denotes the current iteration, A and C are coefficient vectors, and Xp and X are the prey's and the grey wolf's respective position vectors [24]. The following are the formulae used to calculate A and C:…”

Section: Iter= Iter +1mentioning

confidence: 99%

“…These different values of the control gain parameters lead to obtaining different responses with high overshoot, especially in the digital PID controller. In addition, the authors in [10,11] exploited the type-2 fuzzy controller for tracking and stabilizing the blood glucose level in T1D patients. On the other hand, a radial basis function neural network was utilized in [12] as an intelligent controller for an automated insulin delivery system for a virtual patient model to monitor and control the blood glucose level within days.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 shows the parameters of the Bergman model equations that describe normal, first, second, and third patients as follows [8,10,17,18].…”

mentioning

confidence: 99%

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Enhancement of the Blood Glucose Level for Diabetic Patients Based on an Adaptive Auto-Tuned PID Controller via Meta-Heuristic Methods

2024

IJIES

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This work proposes an intelligent method that uses an adaptive auto-tuned proportional-integral-derivative (PID) controller to track and regulate the blood glucose level of diabetic patients. The suggested controller seeks to provide the optimal insulin control action, which is in charge of swiftly, precisely, and accurately managing the blood glucose level. To train this controller, two meta-heuristic techniques are employed. The first technique is the particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second one is an intrusion detection technique called grey wolf optimization (GWO), which was created to categorize data and effectively find multiple intrusions. The mean square error performance index is used in the two distinct meta-heuristic algorithm types to determine and optimize the optimal or nearly optimal gain parameters of the adaptive PID controller. The results of the MATLAB simulations for three different patients demonstrated the efficacy and resilience of the proposed control algorithm in tracking the dynamic behaviour of the diabetic patients' blood glucose levels by minimizing overshoot in the transient state to zero value, maintaining the steady-state blood glucose level in the normal physiological level of (60-120) mg/dl. These characteristics were particularly evident when we added a meal as a disturbance effect. Moreover, the comparison results showed that the proposed PID-GWO and PID-PSO algorithms enhanced the time (96 and 88) minutes to reach the blood glucose level at a normal physiological level by 4% and 12%, respectively, when compared to the fractional order PID and fuzzy logic control algorithms that the blood glucose level reached at a normal physiological level at 100 minutes and improved the time by 20% and 27%, respectively, when compared to the type-2 fuzzy control algorithm that the blood glucose level reached at a normal level at 120 minutes. In particular, the blood glucose level was kept at the desired normal physiological level without any oscillation.

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“…𝑋(𝑖𝑡𝑒𝑟 + 1) = |𝑋 𝑝 (𝑖𝑡𝑒𝑟) − 𝐴 × 𝐷| (10) where the distance between the grey wolf individual and its prey is represented by Eq. ( 9).…”

Section: The Gwo Algorithmmentioning

confidence: 99%

Section: Iter= Iter +1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Table 1 shows the parameters of the Bergman model equations that describe normal, first, second, and third patients as follows [8,10,17,18].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Enhancement of the Blood Glucose Level for Diabetic Patients Based on an Adaptive Auto-Tuned PID Controller via Meta-Heuristic Methods

2024

IJIES

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“…Existing control algorithms can be broadly discerned to be reactive and predictive. Reactive control action includes control algorithms such as proportional-integral and derivative (PID), fuzzy, and other approaches that compute insulin based on current measurements and lack predictive capabilities [ 7 , 8 , 9 ]. Some additional capabilities are available in the reactive controllers as well.…”

Section: Introductionmentioning

confidence: 99%

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Padmapritha

Srinivasan

Kesavadev

et al. 2023

JCM

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Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

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