Asma Atig

University of Tabuk / University of Gabès

Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (PSO) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to acc...

An arbitrary choice of the neural controller adaptive rate can have a negative effect on the performance of the closed-loop system. In this study, we propose a novel methodology for neural controller adaptive rate using Particle Swarm Optimization algorithm. The developed control scheme is composed of a recurrent neural networks emulator and contro...

The application of neural networks can present some limitations for the control of strongly nonlinear systems. In this paper, a new control scheme based on a neural multicontroller (NMC) is proposed. Indeed, the developed strategy considers a set of local neural controllers which adapt their parameters thanks to an online adaptation algorithm. The...

This paper presents a multi-objective indirect neural adaptive control design for nonlinear square multi-variable systems with unknown dynamics. The control scheme is made of an adaptive instantaneous neural emulator, a neural controller based on fully connected real-time recurrent learning (RTRL) networks and an online parameter updating law. A mu...

In this paper, a stability analysis strategy of nonlinear control systems is proposed. An adaptive neural control scheme composed of an emulator, and a controller with decoupled adaptive rates is considered. A Lyapunov function based on tracking error dynamic is retained and an online adjusting technique of the neural controller adaptive rate is ad...

In this study, an adaptive control based on fuzzy adapting rate for neural emulator of nonlinear systems having unknown dynamics is proposed. The indirect adaptive control scheme is composed by the neural emulator and the neural controller which are connected by an autonomous algorithm inspired from the real-time recurrent learning. In order to ens...

This paper deals with a new fuzzy adapting rate for a neural emulator of nonlinear systems with unknown dynamics. This method is based on an online intelligent adaptation by using a fuzzy supervisor. The satisfactory obtained simulation results are compared with those registered in the case of the classical choice of adapting rate and show very goo...

In this paper, multimodel and neural emulators are proposed for uncoupled multivariable nonlinear plants with unknown dynamics. The contributions of this paper are to extend the emulators to multivariable non square systems and to propose a systematic method to compute the multimodel synthesis parameters. The effectiveness of the proposed emulators...

This paper deals with the adaptive control of single-input multi-output (SIMO) underactuated nonlinear systems. The restriction of the control authority for these systems causes major difficulties in control design. In this work, we propose an adaptive neural controller based on neural emulator to solve the control problems for a class of SIMO nonl...

This work describes multivariable adaptive neural control based on multimodel emulator for nonlinear square MIMO systems. Multimodel approach is an interesting alternative and a powerful tool for modelling and emulating complex processes. This paper deals with the identification of nonlinear MIMO systems employing an uncoupled multimodel. Efficienc...

In this paper, multimodel and neural emulators are proposed for nonlinear plants with unknown dynamics. The contribution of this paper is to extend the emulators to multi-variable non square systems. The effectiveness of the proposed emulators are shown through an illustrative simulation example. The obtained results are very satisfactory and show...

This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this pape...

This paper adresses a Lyapunov stability analysis of nonlinear systems control. We consider an adaptive control scheme based on recurrent neural networks emulator and controller with decoupled adaptive rates. Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller are proposed. In order to guarantee the...

This work investigates an uncoupled multimodel emulator for non-linear system control design. Efficiency of the proposed multimodel emulator is illustrated by comparison with the neural one by their application to SISO indirect adaptive neural control. Neither an initialisation parameter and nor online adaptation is required for multimodel emulator...

In this paper, we develop an indirect adaptive control struc- ture based on recurrent neural networks. An adaptive emulator inspired from the Real Time recurrent Learning algorithm is presented. Neural network does not learn the plant dynamics but emulates the input-output mapping with a small time window. Thereafter, a controller with a struc- tur...

This paper provides an adaptation algorithm for the control of complex system via recurrent neural networks. The proposed method is derived from RTRL algorithm. Neural emulator and neural controller parameters are one-line updated independently. To illustrate the tracking and the disturbance rejection capabilities of the real time control algorithm...

This paper deals with a new indirect adaptive control scheme with decoupled adaptive rates, developed for complex square systems with unknown dynamics. This scheme, based on fully connected neural networks, is inspired from the real time recurrent learning (RTRL) algorithm. Both neural emulator and neural controller networks do not learn the plant...

In this paper, a real time recurrent learning-based emulator is presented for nonlinear plants with unknown dynamics. This emulator is based on fully connected recurrent neural networks. Starting from zero values, updating rate, time parameter and weights of the instantaneous neural emulator adapt themselves in order to estimate the process output....

This paper addressed nonlinear system identification problem using Volterra models. First, an approach is established to identify Volterra models using "Plant-friendly" input sequence with define properties. Second, we focus on the parametric complexity problem. As a solution, Volterra kernels are projected onto Laguerre basis resulting a reduced V...