Optimal PI-fuzzy logic controller of glucose concentration using genetic algorithm

Al-Fandi, Mohamed; Jaradat, Mohammad A.; Sardahi, Yousef

doi:10.3233/kes-2010-0215

Cited by 8 publications

(26 citation statements)

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“…It is noticeable that almost 1 h after the start, the blood glucose concentration has reached the interval [73.4844 127.3854] mg/dL, which is in the acceptable range. mU/day of insulin was injected by the controller in [29]. This demonstrates the excellence of the SOTFC in this paper.…”

Section: Uncertainty In the Clearance Rate Parametersupporting

confidence: 59%

“…In this case, 826.9351 mU/day of insulin is injected by the insulin infusion pump. However, under the same conditions, 832.3450 mU/day of insulin is infused by the SOTFC in [29]. This shows the advantage of the SOTFC.…”

Section: Uncertainty In the Clearance Rate Parametermentioning

confidence: 77%

“…As shown in [29], the membership functions with various averages and constant variances have better performance in mimicking the blood glucose response of the reference model. Thus, the variance of all Gaussian membership functions is set to 0.3.…”

Section: Performance Analysis Without Uncertaintymentioning

confidence: 99%

“…Moreover, the closed-loop control parameters obtained by LDW-PSO are listed in Table 6. Finally, in order to show the feasibility of the SOTFC, the results are compared with those obtained in [29], which is summarized below: 1) The SOTFC in this paper is much less complicated. As shown in Table 3, the designed controller in this paper has only 9 rules, and with these limited rules, the design requirements are satisfied.…”

Section: Performance Analysis Without Uncertaintymentioning

confidence: 99%

“…As shown in Table 3, the designed controller in this paper has only 9 rules, and with these limited rules, the design requirements are satisfied. In [29], however, for achieving satisfactory results, 49 rules were defined.…”

Section: Performance Analysis Without Uncertaintymentioning

confidence: 99%

See 4 more Smart Citations

Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Khooban¹,

Abadi²,

Alfi³

et al. 2013

Turk J Elec Eng & Comp Sci

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Diabetes is a chronic disease in which there are high levels of sugar in the blood. Insulin is a hormone that regulates the blood glucose level in the body. Diabetes mellitus can be caused by too little insulin, a resistance to insulin, or both. Although research activities on controlling blood glucose have been attempted to lower the blood glucose level in the quickest possible time, there are some shortages in the amount of the insulin injection. In this paper, a complete model of the glucose-insulin regulation system, which is a nonlinear delay differential model, is used.The purpose of this paper is to follow the glucose profiles of a healthy person with minimum infused insulin. To achieve these purposes, an intelligent fuzzy controller based on a Mamdani-type structure, namely the swarm optimization tuned Mamdani fuzzy controller, is proposed for type 1 diabetic patients. The proposed fuzzy controller is optimized by a novel heuristic algorithm, namely linearly decreasing weight particle swarm optimization. To verify the robust performance of the proposed controller, a group of 4 tests is applied. Insensitivity to multiple meal disturbances, high accuracy, and superior robustness to model the parameter uncertainties are the key aspects of the proposed method. The simulation results illustrate the superiority of the proposed controller.

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Section: Uncertainty In the Clearance Rate Parametersupporting

confidence: 59%

Section: Uncertainty In the Clearance Rate Parametermentioning

confidence: 77%

Section: Performance Analysis Without Uncertaintymentioning

confidence: 99%

Section: Performance Analysis Without Uncertaintymentioning

confidence: 99%

Section: Performance Analysis Without Uncertaintymentioning

confidence: 99%

See 3 more Smart Citations

Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Khooban¹,

Abadi²,

Alfi³

et al. 2013

Turk J Elec Eng & Comp Sci

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show abstract

Enhancement of hard disk drive manipulator using piezoelectric actuator mechanisms

Khasawneh

Jaradat

Naji

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

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This paper discusses the employment of piezoelectric actuators (PEA) to encompass high precision-scale movement capability of magnetic hard disk drive servo systems. To overcome path/positioning limitations of previous hard disk servo systems, a new design consisting of three PEAs links is proposed. Two different controllers, namely PI and fuzzy, are designed and simulated to assess the performance of the proposed design over a couple of paths under the presence of noise and disturbance. Simulation results show that the proposed design is able to track complex paths over a wider spectrum of motion than conventional hard drive servo systems. This improves the read-write process by affording nanoscale motion and provides high levels of positioning accuracy, as well as wide spectrum of positioning capability. Moreover, both controllers reveal the ability to drive the mechanism with quick response and minimal overshoot, with the fuzzy controller outperforming the PI controller under the presence of noise and disturbance.

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Optimal design of IT2‐FCS‐based STATCOM controller applied to power system with wind farms using Taguchi method

Hong

Nguyen

2018

IET Generation, Transmission & Distribution

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Optimal PI-fuzzy logic controller of glucose concentration using genetic algorithm

Cited by 8 publications

References 0 publications

Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Enhancement of hard disk drive manipulator using piezoelectric actuator mechanisms

Optimal design of IT2‐FCS‐based STATCOM controller applied to power system with wind farms using Taguchi method

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