Ali Zribi

• Institut Supérieur des Sciences Appliquées et de Technologie de Gabès

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to...

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to min...

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...

In the past few years and in many applications, linear feedback control has proved to be adequate for the control of various nonlinear systems. Therefore, before attempting the controller design phase, it is natural to ask, “When is a linear controller adequate for a nonlinear system?” This article provides a new nonlinearity analysis method based...

This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real-time recurrent learning (RTRL). The proposed method is formulated by an inequality-constraint optimization problem where the Lagrang...

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...

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...

This paper deals with the control of nonlinear systems where the multimodel approach has been used to build global controller. In multimodel approaches, two problems could be generally encountered: (1) how to find the required number of models and (2) what are their locations in an operating space. The developed method integrates gap metric, margin...

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and...

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and...

In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), an adaptive neural network internal model control (NNIMC) is designed for a permanent magnet synchronous motor (PMSM). Firstly, in order to accelerate the convergent speed and to prevent problems of trapping in local minimum, PSO algorith...

In this paper, a new validity criterion determining the optimal decomposition and the minimum linear model bank was employed to design a controller for a nonlinear system. Based on the gap metric, the proposed validity criterion quantifies the compactness degree for a model-base. For a decomposition giving a low degree of compactness, the optimal l...

This paper deals with the control of nonlinear systems using multimodel approach. The main idea of this work consists on the association of the gap metric and the stability margin tools to reduce the number of models constituting the multimodel bank. In fact, the self-organisation map (SOM) algorithm is used, firstly, to develop a preliminary multi...

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The presented method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance an...

This paper proposes a novel gap metric based fuzzy decomposition approach resulting in a reduced model bank that provides enough information to design controllers. It requires, first, the determination of the model base. For this, the number of initial models is obtained via fuzzy c-means (FCM) algorithm. Then, a gap metric based method which aims...

In this paper, an internal multiple model control (IMMC) based on linear model's library is introduced. This approach supposes the definition of a set of local linear models. However, it remains beset with several difficulties such as the determination of the local models base. A new approach that combines fuzzy c-means (FCM) clustering algorithm a...

This paper deals with clustering of data using different techniques. It firstly presents popular unsupervised clustering algorithms, such as neuro-fuzzy and Fuzzy C-Means (FCM) techniques. Then a hybrid clustering algorithm, starting with the neuro-fuzzy approach and ending with the FCM one, is presented. Secondly, Agglomerative Clustering (AC) and...

For linear plants, IMC have been shown good robustness properties against disturbances and model mismatches. However, when uncertain processes are concerned, the original IMC structure cannot be directly used for control system implementation. In this paper, an internal multiple model control (IMMC) based on linear model's library is introduced. Th...

An Internal Multiple Model Control (IMMC) based on Robust Clustering Algorithm (RCA) is proposed. The IMMC requires, firstly, the definition of set a of local models each one is valid in a given region. Different strategies exist in the literature dealing with the determination of the local models base. However, most of these strategies need a-prio...

This paper presents popular unsupervised clustering algorithms based on neuro-fuzzy, fuzzy c-means (FCM) and agglomerative techniques. The purpose of this paper is to provide clustering methods able to cluster the data patterns without a priori information about the number of clusters. We will show that it is possible to reconcile the FCM algorithm...