An intelligent approach for optimal regulation of blood glucose level

Beyki, Karim; Javan, Mohammad Dashti; Kambiz, Shojaee G.; Neshati, Mohammad Mohsen

doi:10.1109/icbme.2010.5704984

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Theorem 1: Consider the fractional order nonlinear uncertain system with unknown dynamics (13) and nonlinear neuro-adaptive observer (20). If the weights of the multilayer perceptron were updated via adaptive law (27), then x, w, and e are guaranteed as uniformly ultimately bounded .…”

Section: Fig 2 Structure Of the Neural Observermentioning

confidence: 99%

“…the time derivative of function ( 28) can be written by: by substituting equations ( 20), (27), and ẇ = − ẇ into (35), it gives:…”

Section: Fig 2 Structure Of the Neural Observermentioning

confidence: 99%

“…In the study [26], for reduction of the time of descending blood glucose, a PID controller based on neural networks is suggested. In [27], for minimizing the time that lasts for blood glucose to return to its basic level, several parameters of the Hammerstein controller were optimized. In [31], an online glucose-insulin identification using the Recurrent High Order Neural Network is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

khoygani

Khoygani

Ghasemi

2021

IJRCS

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Diabetes is an increasing health problem all around the world, particularly Type 1 diabetes (T1D), people with T1D require precise glycemic control, due to a shortage of insulin production. This paper introduces a new adaptive neural observer-based controller for a class of nonlinear T1D systems. A solution is proposed to guarantees practical tracking of a desired glucose concentration by a new adaptive neural observer-based control strategy. One of the intelligence procedures is the network under online learning that the mentioned controller is learned by a back-propagation algorithm. This network is a significant class of feed-forward artificial neural networks that maps a set of inputs into a set of proper outputs. Guarantee stability of observer and controller by Lyapunov direct and training online are the merit of the method. Also, despite the presence of internal and external uncertainties, the multilayer perceptron neural observer-based controller is robust. The performance of the proposed method is hopeful based on the results.

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Section: Fig 2 Structure Of the Neural Observermentioning

confidence: 99%

“…the time derivative of function ( 28) can be written by: by substituting equations ( 20), (27), and ẇ = − ẇ into (35), it gives:…”

Section: Fig 2 Structure Of the Neural Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

khoygani

Khoygani

Ghasemi

2021

IJRCS

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show abstract

“…In [6] a PID controller based on BP neural networks is proposed in order to reduce the time of lowering blood glucose. In [11], the parameters of Hammerstein controller were optimized in order to minimize the time that takes for blood glucose to come back to its basal level. Also there are some efforts to use model independent based controller such as fuzzy controllers.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Different Advanced Control Schemes for Glucose Level Control under Disturbing Meal

Midhat¹,

Humaidi²

2017

alkej

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Abstract In this work, diabetic glucose concentration level control under disturbing meal has been controlled using two set of advanced controllers. The first set is sliding mode controllers (classical and integral) and the second set is represented by optimal LQR controllers (classical and Min-, ax). Due to their characteristic features of disturbance rejection, both integral sliding mode controller and LQR Minmax controller are dedicated here for comparison. The Bergman minimal mathematical model was used to represent the dynamic behavior of a diabetic patient’s blood glucose concentration to the insulin injection. Simulations based on Matlab/Simulink, were performed to verify the performance of each controller. In spite that Min-max optimal controller gave better disturbance rejection capability than classical optimal controller, classical sliding mode controller could outperform Min-max controller. However, it has been shown that integral sliding mode controller is the best of all in terms of disturbance rejection capability. Key words: Optimal LQR control, Optimalminimax control, Sliding mode control, Integral sliding mode control.

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Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients

2016

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An intelligent approach for optimal regulation of blood glucose level

Cited by 6 publications

References 17 publications

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

Intelligent Observer-Based Controller Design for Nonlinear Type-1 Diabetes Model via Adaptive Neural Network Method

Performance Comparison of Different Advanced Control Schemes for Glucose Level Control under Disturbing Meal

Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients

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