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

Abstract

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.