Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control. Issue 1 (1st February 2020)

• Record Type: Journal Article
• Authors: Karsaz, Ali
• Is Part Of: IET systems biology. Volume 14:Issue 1(2020)
• Page Start: 31
• Page End: 38
• Publication Date: 2020-02-01
• Subjects: fuzzy control -- variable structure systems -- particle swarm optimisation -- neurocontrollers -- fuzzy neural nets -- blood -- genetic algorithms -- closed loop systems -- medical control systems -- fuzzy reasoning -- diseases -- nonlinear control systems -- sugar; data fusion -- adaptive neuro‐fuzzy inference systems -- particle swarm optimisation -- hybrid controller -- desired BG‐level tracking -- chattering reduction -- daily glucose‐level disturbances -- closed‐loop control scheme -- glucose–insulin regulatory system -- type‐1 diabetic mellitus patients -- innovative hybrid glucose–insulin regulators -- artificial intelligence -- fuzzy logic -- genetic algorithm -- Palumbo model -- blood glucose level -- T1DM patients -- glucose reference tracking -- insulin injection -- mode controllers -- glucose–insulin mechanism -- chattering‐free hybrid adaptive neuro‐fuzzy inference system -- particle swarm optimisation data fusion‐based BG‐level control; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2018.5019
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.