Ben Abdennour

• Professor
• École Nationale d'Ingénieurs de Gabès

This paper is devoted to sensor fault detection for nonlinear systems using an interval multiobserver based on uncoupled multimodel. This proposed interval multiobserver is used to detect the faults through residual intervals multigenerator. The [Formula: see text] performance technique is introduced to improve the accuracy of the sensor fault dete...

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...

The difficulty of observer design for a general class of nonlinear system and the reduction of chattering phenomenon are still challenging tasks in the theory of sliding mode observers. Thus, this paper aims to develop a novel second order sliding mode multiobserver (2SMMO) in order to avoid these problems. Indeed, a decoupled multimodel approach i...

The design of an accurate observer is still a challenging problem for nonlinear systems subject to fault signals. This paper addresses the problem of simultaneous state and sensor fault estimation for discrete-time nonlinear systems. An uncoupled multimodel approach is adopted to deal with nonlinear systems subject to sensor faults. A simple sensor...

The present work focuses on the design of an interval multiobserver for nonlinear systems with internal and external disturbances. The structure of the developed interval multiobserver is based on a discrete uncoupled multimodel. The used multimodel is known for its flexibility in the modeling step, since the state variables of the partial models c...

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 chattering phenomenon is one of the most pervasive problems in sliding mode control (SMC), especially for discrete time applications. In this paper, we propose a new discrete output feedback second order sliding mode control (DOF2SMC). The design of this controller is based on the Linear Matrix Inequalities (LMI) approach. Then, an adaptive swi...

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...

The present paper deals with the design of sliding mode multiobserver for nonlinear systems with delayed measurements. The discrete uncoupled state multimodel is exploited to describe the global behavior of nonlinear systems. Therefore, the complexity of the latter can be reduced by its decomposition into a finite number of partial models. The prop...

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...

The identification and the design of an accurate control are still an active researches of nonlinear systems subject to harmonic disturbances. In this paper, we propose two techniques to deal with nonlinear system identification and control problems. First, a new identification scheme based only on input/output measurements is proposed. In this sch...

An active sensor fault tolerant controller for nonlinear systems represented by a decoupled multimodel is proposed. Active fault tolerant control requires accurate fault estimation. Thus, to estimate both state variables and sensor faults, a discrete unknown input multiobserver, based on an augmented state multimodel, is designed. The multiobserver...

In this paper, a sliding mode observer for discrete-time multivariable systems is proposed. A linear matrix inequalities based on the Lyapunov theory for the design of asymptotically stable sliding mode observer is formulated and the stability of reconstruction error systems is discussed. The convergence analysis of the proposed observer is develop...

In this paper, a sensor fault detection and isolation scheme based on discrete unknown input multiobservers for a disturbed nonlinear systems is proposed. The proposed multiobservers are based on an uncoupled state multimodel. Sensor faults can be isolated using the generated structured residuals. Nevertheless, the presence of disturbances make the...

In the present paper, an unknown input multiobserver (UIMO) is designed for the state estimation of uncertain non-linear systems. A discrete decoupled state multimodel is exploited to describe the behavior of non-linear systems. A particular transformation of uncertainties to unknown inputs is considered. The LMI approach is used to establish the c...

This paper concerns the design of robust sliding mode multiobserver for nonlinear systems. A discrete uncoupled multimodel structure is retained for the modeling of nonlinear systems. Unlike the classically used multimodel structures, the retained uncoupled multimodel is known by its flexibility of modeling, thus, the structures of the partial mode...

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...

This paper deals with the robustness enhancement of proportional Q-integral multiobserver in the case of non-stationary sinusoidal unknown inputs. The multimodel approach is proposed in order to overcome the complexity problems of nonlinear systems. The proposed multiobserver uses the multi-integral strategy in order to provide a simultaneous estim...

In this paper, we propose a new method for an optimal systematic determination of models' base for multimodel identification. This method is based on the neural classification of data set picked out on a considered nonlinear system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispers...

In this paper, an online algorithm is proposed for the identification of unknown time-varying input delay in the case of discrete non-linear systems described by decoupled multimodel. This method relies on the minimization of a performance index based on the error between the real system and the partial internal models outputs. In addition, a decou...

This thesis deals with the synthesis of unknown input multiobservers and multimodel control scheme in the case of nonlinear systems. A study to minimize the impact of a non-stationary sinusoidal unknown input on the estimation error is proposed. Indeed, an improvement of the robustness of a multi-integral proportional multiobserver in the case of n...

In this paper, a robust multiobserver is proposed for the state estimation of discrete-time uncertain nonlinear systems with time-varying delay. The designed multiobserver is based on the decoupled multimodel approach. Unlike the classically used multimodel structures, the decoupled multimodel provides a flexibility of modelling. Indeed, the partia...

In the present work, we propose a supervised multimodel repetitive-predictive control scheme for discrete-time nonlinear systems in order to reject unknown non-stationary sinusoidal disturbances and to track reference trajectory. The nonlinear system is described by the decoupled state multimodel. Also, a non-stationary sinusoidal unknown input mul...

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...

Simple high gain observer-based estimators which allow the estimation of the reaction rates from the measurements of component concentrations inside bioreactors are presented. The main properties of these estimators lie in the fact that the measurements of the component concentrations are non available in a continuous manner, as generally assumed i...

In this paper, a multi-rejector of periodic disturbances is proposed for discrete-time nonlinear systems represented by a decoupled state multimodel. We report a decoupled state multimodel repetitive-predictive control based on a supervised algorithm to ensure reference trajectory tracking and periodic disturbances rejection. Partial predictors ass...

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...

A continuous–discrete time observer is proposed for a class of uncertain nonlinear systems where the output is available only at non uniformly spaced sampling instants. The underlying correction term depends on the output observation error and is updated in a mixed continuous-discrete fashion. The proposed observer is first introduced under a set o...

A continuous-discrete time observer is proposed for a class of uncertain nonlinear systems where the output is available only at non uniformly spaced sampling instants. The underlying correction term depends on the output observation error and is updated in a mixed continuous-discrete fashion. The proposed observer is first introduced under a set o...

A new general relationship between cumulants and impulse response coefficients of the FIR systems is presented. This relation can be used to unify several of the FIR coefficients estimation algorithms proposed in the literature. It can be further used to develop a new estimation method. This relation is illustrated with a few examples.

The present paper deals with a decoupled multimodel predictive control based on multi-observer for the control of discrete-time nonlinear systems with time-varying delay. For each local model, a controller based on partial predictor/observer is synthesized. A switching algorithm is established to yield the adequate partial controller ensuring the c...

This paper deals with the robustness enhancement of proportional Q-integral multiobserver in the case of non-stationary sinusoidal unknown inputs. The multimodel approach is proposed in order to overcome the complexity problems of nonlinear systems. The proposed multiobserver uses the multi-integral strategy in order to provide a simultaneous estim...

This paper deals with a decoupled multimodel predictive control for discrete-time uncertain nonlinear systems. The control scheme is based on a multi-observer for the state estimation of uncertain nonlinear systems described by decoupled multimodel. A partial controller and observer is synthesized for each local model. In order to ensure the closed...

This paper deals with the design of robust non-stationary sinusoidal unknown inputmultiobserver for discrete-time nonlinear systems described by decoupled state multimodel. The particularity of this work resides in the fact that it provides a simultaneous state and non-stationary sinusoidal unknown inputs estimations. A new robust strategy allowing...

In this paper, one aims at addressing the adaptive observer design for a class of multivariable nonlinear systems with Lipschitz nonlinearities. Two adaptive observers have been derived using two different approaches issued from an overview of the available adaptive observer design literature. The first one is a natural extension of a recently prop...

The multimodel approach is a powerful and practical tool to deal with analysis, modeling, observation, emulation and control of complex systems. In the modeling framework, we propose in this paper a new method for optimal systematic determination of models’ base for multimodel representation. This method is based on the classification of data set p...

In this paper, we propose an adaptive observer for a class of uniformly observable nonlinear systems with nonlinear parametrization and sampled outputs. A high gain adaptive observer is first designed under the assumption that the output is continuously measured and its exponential convergence is investigated, thanks to a well defined persistent ex...

This paper presents a continuous-discrete time observer for a class of uncertain nonlinear systems with non uniformly sampled measurements. Two features of the proposed observer are worth to be pointed out. The first one consists in the simplicity of its calibration, while the second one lies in its comprehensive convergence analysis. More specific...

In this work, a nonstationary sinusoidal unknown inputs multiobserver is proposed for discrete-time nonlinear systems represented by a decoupled multimodel. The existence conditions of this type of multiobserver will be addressed and a new strategy allowing the minimization of the impact of nonstationary sinusoidal unknown inputs on the state estim...

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 proposes a state observer with a cas- cade structure for a class of continuous time dynamical systems with non-uniformly sampled delayed output measurements. The first subsystem in the cascade is an impulsive observer which provides an estimation of the delayed state. Each remaining subsystem in the cascade is a predictor, which estimate...

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...

A high gain like observer with updated gain is proposed for a class of cascade nonlinear and non triangular systems that are observable for any input. The objective of the gain adaptation is to perform an admissible tradeoff between state reconstruction dynamics on the one hand versus noise amplification on the other hand. To this end, the gain of...

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...

This paper concerns the chattering elimination from discrete sliding mode observers. The dilemma chattering-precision, that characterizes the first order sliding mode observer in case of relatively large parameter variations and/or external disturbances, is discussed and the influence of the discontinuous term amplitude on the estimation performanc...

In this work, a second order asymptotic discrete sliding mode control with a fuzzy supervised discontinuous term is proposed. The on line variation of the discontinuous term amplitude enhances the convergence rapidity of the sliding function and reduces the chattering phenomenon. These improvements are shown by a numerical simulation and by a real...

In order to improve the robustness of the discrete high gain observer with respect to noise measurements and enhance the convergence dynamics, we propose, in this paper, a fuzzy supervision for the observation of a class of nonlinear systems. Actually, a suitable tuning of the observer’s gain is made by a fuzzy supervisor. Simulation results are gi...

The multimodel approach is recently developed in order to resolve complexity of many industrial processes. Nevertheless, this approach is often confronted to several difficulties, such as, the determination of the useful models' base. A new approach for determination of a models' base for the representation of non stationary time delay systems is p...

A high gain like observer with an updated gain is proposed for a class of MIMO nonlinear systems that are observable for any inputs. The main contribution of this article lies in the nature of the observer gain that involves a scalar time-varying design parameter governed by some scalar Riccati equation. This time-varying design parameter, chosen c...

Reference trajectory asymptotic tracking and disturbance re-jection are an important field of control theory. In this paper, we are interested on the tracking of reference trajectories and the rejection of periodic disturbances with unknown frequency for multivariable systems. If the frequency is known, a classical solution is to use a multivariabl...

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....

In this paper, the chattering phenomenon that characterizes discrete sliding mode systems is analyzed and illustrated by a simulation example. In order to resolve this problem, in case of relatively large parameter variations and/or external disturbances, a second order discrete sliding mode observer (2-DSMO) is proposed. A stability analysis of th...

The temperature control in chemical reactors has been shown to be of fundamental importance from product quality and process reproducibility points of view. This paper presents an experimental evaluation of multimodel approach involving a batch chemical reactor pilot. Two design features are worth to be mentioned. Firstly, an appropriate system ide...

Temperature control of processes that involve heating and cooling of semi-batch reactors can be a real problem for conventional controllers that do not put up with relatively large model uncertainty and external disturbances. The first order sliding mode control can be a solution for this problem; however, discrete-time implementation generates the...

A dynamic high gain based observer is proposed for the class of uniformly observable systems which are observable for any inputs. The main feature of this observer consists in an appropriate calibration of the observation gain through a single parameter governed by some scalar Riccati equation. Simulation results are given in order to highlight the...

The multimodel approach has become a major research topic during the last few decades and unlike many other advanced techniques, it has also been successfully applied in industry. This paper describes the application of a multimodel generalised predictive control (MGPC) based on a commutation algorithm to the problem of a semi-batch reactor control...

The esterification reaction requires a tight temperature control. As the different stages of this reaction (the heating, the reaction and the cooling) are characterised by different dynamics, a discrete second order sliding mode control using one global model of the system was not able to ensure the desired performances. We propose, in this paper,...

This paper investigates experimentally the identification and the control of a semi-batch chemical reactor based on multiple model and multiple control approaches. This non-linear and time-dependent process is described by three simple and linear local models representing each one a specific operating zone. The developed multiple model and multiple...

In this work, an asymptotic numerical second order sliding mode control (2-DSMC) is developed in order to reduce the chattering phenomenon that characterize the discrete first order sliding mode control. A comparative study with Bartolinipsilas numerical second order sliding mode control approach is realized. The obtained results in the case of the...

In this work, the multimodel approach is exploited in order to ameliorate the discrete second order sliding mode control (2-DSMC) performances in the case of highly non stationary systems. Simulation results show a notable improvement relatively to the classical 2-DSMC, especially in the reaching phase.

The multimodel approach is recently developed in order to resolve the problems of the increasing complexity of many industrial processes. In this paper we propose a multi- model generalized predictive control based on a commutation algorithm. This commutation is controlled by partial predictors associated to the local controllers. These predictors...

In order to improve the robustness of the Discrete Sliding Mode Control, essentially in the reaching phase, we propose in this paper a new version based on the multimodel and multicontrol approaches. A real time application of the Multimodel Discrete Sliding Mode Control (MM-DSMC) and of the Classical Discrete Sliding Mode Control (C-DSMC) is reali...

This article presents a new approach of systematic determination of models base for the multimodel approach. The application of this approach requires, first, to classify a numeric data by exploiting the self-adapting artificial Kohonen neural-networks. The obtained data relative to the clusters are then exploited for both structural and parametric...

In this paper, we develop a direct adaptive generalized pre-dictive control based on performances index and we show the limits of this control law in the presence of a highly non stationary system. We present thereafter a method for validities estimation in the case of multi-model generalized predictive control (MGPC). This method is based on an on...

In this paper, we propose a methodology relative to the ap- plication of a sliding mode control to linear multivariable systems. This methodology is based on the decomposition of the system in several subsystems controllable each by only one component of the input. The application of the proposed strategy in the case of a sliding mode con- trol to...