This paper focusses on a novel discrete-time reduced order neural observer for nonlinear
systems, which model is assumed to be unknown. This neural observer is robust in …

In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is
proposed. The proposed scheme is based on a discrete-time recurrent high order neural …

This paper proposes an impulsive control scheme for a complex network that helps reduce
the spread of two epidemic diseases: influenza type A and COVID-19. Both are respiratory …

This paper focusses on recurrent adaptive neural control applied to unknown nonlinear
systems with input constraints. A recurrent high‐order neural network is used in order to …

This paper presents the design of an adaptive controller based on the block control
technique, and a new neural observer for a class of MIMO discrete-time nonlinear systems …

In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is
proposed. The main objective is to estimate variables of methanogenesis: biomass …

This paper focusses on a discrete‐time neural identifier applied to a linear induction motor
(LIM) model, whose model is assumed to be unknown. This neural identifier is robust in …

This paper presents the design of an adaptive controller based on the block control
technique, and a new neural observer for a class of MIMO discrete-time nonlinear systems …

A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction
motor model, in presence of external and internal uncertainties is presented. The observer is …

A control strategy, composed by a neural observer and a fuzzy supervisor, for an anaerobic
process is proposed in this paper. A recurrent high order neural observer (RHONO) is …