A Novel Neuroglial Architecture for Modelling Singular Perturbation System

Abstract

<p class="Abstract">This work develops a new modular architecture that emulates a recently-discovered biological paradigm. It originates from the human brain where the information flows along two different pathways and is processed along two time scales: one is a fast neural network (NN) and the other is a slow network called the glial network (GN). It was found that the neural network is powered and controlled by the glial network. Based on our biological knowledge of glial cells and the powerful concept of modularity, a novel approach called artificial neuroglial Network (ANGN) was designed and an algorithm based on different concepts of modularity was also developed. The implementation is based on the notion of multi-time scale systems. Validation is performed through an asynchronous machine (ASM) modeled in the standard singularly perturbed form. We apply the geometrical approach, based on Gerschgorin’s circle theorem (GCT), to separate the fast and slow variables, as well as the singular perturbation method (SPM) to determine the reduced models. This new architecture makes it possible to obtain smaller networks with less complexity and better performance.</p>

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

“…A main objective of feedback system design is to achieve a nominal performance specification for a given design model of the plant, and to maintain this performance over a range of expected errors between the design model and the true plant [18][19][20][21]. In this section, three control methods are proposed and described in brief: LQI controller, LQG and LTR based on optimal control theory, the proposed approach in this paper is detailed in the scheme presented in Figure 4.…”