Farouk ZOUARI
@enit.rnu.tn
Université de Tunis El Manar, BP. 37, Le Belvédère, 1002 Tunis, Tunisia
Laboratoire de Recherche en Automatique (LARA), École Nationale d’Ingénieurs de Tunis (ENIT), Université de Tunis El Manar, BP. 37, Le Belvédère, 1002 Tunis, Tunisia
Farouk Zouari was born in Tunis, Tunisia, on August 27, 1980. He received his Engineer degree in Electrical Engineering, his magister degree in Automatic and Signal Processing, and his PhD degree in Electrical Engineering from the National Engineering School of Tunis, University of Tunis El Manar, Tunisia, in 2004, 2005 and 2014, respectively. He is currently a researcher at Laboratoire de Recherche en Automatique (LARA), École Nationale d′Ingénieurs de Tunis, Université de Tunis El Manar. His current research interests include fractional-order systems, neural control theory, nonlinear control, and intelligent adaptive control.
EDUCATION
PhD in Electrical, Electronics, and Computer Engineering,
National School Engineers of Tunis, University of Tunis El Manar, Tunisia
Subject: On adaptive neural control of complex dynamic systems
Date of the defense: December 16, 2014
Mention: Right Honorable
Master's degree in electrical engineering,
National School Engineers of Tunis, University of Tunis El Manar, Tunisia
Subject: Implementation of conventional and unconventional identification methods
Date of the defense: August 04, 2005
Mention: Very Good
Electrical engineering bachelor's degree,
National School of Engineers of Tunis, University of Tunis El Manar, Tunisia
Subject: Algorithms for designing artificial neural networks
Date of the defense: June 22, 2004
Baccalaureate Diploma in Mathematics, Tunisia
Graduation Year: 1999
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Oumeima Toumia and Farouk Zouari
IGI Global
Decision making in venture capital involves a lot of work. Venture capitalists must consider a number of issues when selecting an investment. To date, however, little research has been conducted on how artificial intelligence would impact the venture capital decision-making market. Therefore, this chapter extends previous contributions aimed at exploring the relationship between artificial intelligence and venture capital decisions. Evidence shows that artificial intelligence may influence venture capitalists' decisions in a number of ways, such as recognizing firms with high chances of success, assisting venture capitalists in choosing better investments, etc. However, there are also a number of barriers that venture capitalists face in adopting artificial intelligence. The originality of this chapter is the development of items that can be used to measure stages of artificial intelligence. Indeed, it provides some recommendations for how best to integrate artificial intelligence into the decision-making process of venture capitalists.
G. Rigatos, M. Abbaszadeh, K. Busawon, L. Dala, J. Pomares, and F. Zouari
ASME International
Abstract The control problem for the multivariable and nonlinear dynamics of unmanned rotorcrafts is treated with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of 6DOF autonomous quadrotors is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the second subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is further confirmed through simulation experiments showing precise tracking of 3D flight paths by the 6DOF quadrotor.
G. Rigatos, M. Abbaszadeh, B. Sari, P. Siano, G. Cuccurullo, and F. Zouari
Elsevier BV
Farouk ZOUARI, Asier IBEAS, Abdesselem BOULKROUNE, Jinde CAO, and Mohammad Mehdi AREFI
Elsevier BV
G. Rigatos, F. Zouari, G. Cuccurullo, P. Siano, and T. Ghosh
AIP Publishing
G. Rigatos, P. Siano, F. Zouari, and Sul Ademi
Springer Science and Business Media LLC
Mohammed Haddad, Farouk Zouari, Abdesselem Boulkroune, and Sarah Hamel
Springer Science and Business Media LLC
Farouk Zouari, Asier Ibeas, Abdesselem Boulkroune, Jinde Cao, and Mohammad Mehdi Arefi
Elsevier BV
A. Boubellouta, F. Zouari, and A. Boulkroune
Informa UK Limited
ABSTRACT In this research work, a novel fuzzy adaptive control is proposed to achieve a projective synchronization for a class of fractional-order chaotic systems with input nonlinearities (dead-zone together with sector nonlinearities). These master-slave systems under consideration are supposed to be with distinct models, different fractional-orders, unknown models, and dynamic external disturbances. The proposed control law consists of two main terms, namely: a fuzzy adaptive control term for appropriately approximating the uncertainties and a fractional-order variable-structure control term for robustly dealing with these inherent input nonlinearities. A Lyapunov approach is used to derive the updated laws and to prove the stability of the closed-loop control system. At last, a set of computer simulation results is carried out to illustrate and further validate the theoretical findings.
G. Rigatos, P. Siano, P. Wira, M. Abbaszadeh, and Farouk Zouari
IEEE
Control of the milling process of mining products (ore milling) is a non-trivial problem due to being related with a strongly nonlinear and multivariable state-space model. To provide an efficient solution to this problem, in this article a nonlinear optimal (H-infinity) control method is developed. In the considered nonlinear optimal control method, the dynamic model of the mining products' mill undergoes first approximate linearization with the use of Taylor series expansion and with the computation of the associated Jacobian matrices. The linearization point (temporary equilibrium) is recomputed at each time step of the control method and comprises the present value of the system's state vector and the last value of the control inputs' vector that was exerted on it. For the linearized description of the mill's functioning the optimal control problem is solved by applying an H-infinity controller. The feedback gain is computed again at each iteration of the control algorithm through the solution of an algebraic Riccati equation. The stability of the control scheme is confirmed through Lyapunov analysis. First, it is shown that the control method satisfies the H-infinity tracking performance, and this signifies elevated robustness against model uncertainty and external perturbations. Next, under moderate conditions, it is proven that the control loop is globally asymptotically stable.
Farouk Zouari, Asier Ibeas, Abdesselem Boulkroune, Jinde Cao, and Mohammad Mehdi Arefi
Elsevier BV
Farouk Zouari and Amina Boubellouta
IGI Global
In this chapter, an adaptive control approach-based neural approximation is developed for a category of uncertain fractional-order systems with actuator nonlinearities and output constraints. First, to overcome the difficulties arising from the actuator nonlinearities and nonaffine structures, the mean value theorem is introduced. Second, to deal with the uncertain nonlinear dynamics, the unknown control directions and the output constraints, neural networks, smooth Nussbaum-type functions, and asymmetric barrier Lyapunov functions are employed, respectively. Moreover, for satisfactorily designing the control updating laws and to carry out the stability analysis of the overall closed-loop system, the Backstepping technique is used. The main advantage about this research is that (1) the number of parameters to be adapted is much reduced, (2) the tracking errors converge to zero, and (3) the output constraints are not transgressed. At last, simulation results demonstrate the feasibility of the newly presented design techniques.
Farouk Zouari and Amina Boubellouta
IGI Global
This chapter focuses on the adaptive neural control of a class of uncertain multi-input multi-output (MIMO) nonlinear time-delay non-integer order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. The design of the controller follows a number of steps. Firstly, based on the semi-group property of fractional order derivative, the system is transformed into a normalized fractional order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real (SPR) condition, the Razumikhin lemma, the frequency distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof.
Farouk Zouari, Abdesselem Boulkroune, Asier Ibeas, and Mohammad Mehdi Arefi
Springer Science and Business Media LLC
Gerasimos Rigatos, Pierluigi Siano, Farouk Zouari, and Sul Ademi
IEEE
A nonlinear H-infinity (optimal) control method is developed for the problem of simultaneous control of the depth and heading angle of an autonomous submarine. This is a multi-variable nonlinear control problem and its solution allows for precise underwater navigation of the submarine. The submarine's dynamic model undergoes approximate linearization around a temporary equilibrium that is recmputed at each iteration of the control algorithm. The linearization procedure is based on Taylor series expansion and on the computation of the submarine's model Jacobian matrices. For the approximately linearized model, the optimal control problem is solved through the design of an H-infinity feedback controller. The computation of the controller's gain requires the solution of an algebraic Riccati equstion, which is repetitively performed at each step of the control method. The stability of the control scheme is proven through Lyapunov analysis. First, it is demonstrated that for the submarine's control loop, the H-infinity tracking performance criterion holds. Moroever, under moderate conditions it is shown that that the control scheme is globally asymptotically stable.
L. Merazka, F. Zouari, and A. Boulkroune
IEEE
We develop a fuzzy adaptive output-feedback control methodology for unknown nonlinear multivariable systems for which the input gains matrix is characterized by non-zero leading principle minors but not necessary symmetric. An high-gain (HG) observer is introduced to estimate the immeasurable states. A linear in parameters fuzzy system is adequately employed to model the uncertainties. A matrix factorization, so-called SDU, is used when designing the controller to factor the input gains matrix. An appropriate Lyapunov function is exploited to study the stability of the corresponding closed-loop control system as well as to derive the adaptation laws. A 2 DOF helicopter system is used to validate, in a simulation framework, the performances of our developed control approach.
L. Merazka, F. Zouari, and A. Boulkroune
IEEE
In this research, we suggest a new fuzzy adaptive state-feedback control strategy for unknown nonlinear multivariable systems for which the input-gains matrix is not necessarily symmetric and is characterized by non-zero leading principle minors. A linearly parameterized fuzzy system is used to appropriately model the uncertainties. When designing our control scheme and studying the stability analysis, a decomposition property of the input-gain matrix is employed. A proportional-integral (PI) adaptation law is suggested to enhance the adaptive parameter convergence. An appropriate Lyapunov function is exploited to study the stability of the corresponding closed-loop control system as well as to derive the adaptation laws. Numerical simulations and a detailed comparison study are given to evaluate the efficiency of our suggested control methodology.
Farouk Zouari, Abdesselem Boulkroune, and Asier Ibeas
Elsevier BV
Abdesselem Boulkroune, Sarah Hamel, Farouk Zouari, Abdelkrim Boukabou, and Asier Ibeas
Hindawi Limited
This paper solves the problem of projective lag-synchronization based on output-feedback control for chaotic drive-response systems with input dead-zone and sector nonlinearities. This class of the drive-response systems is assumed in Brunovsky form but with unavailable states and unknown dynamics. To effectively deal with both dead-zone and sector nonlinearities, the proposed controller is designed in a variable-structure framework. To online learn the uncertain dynamics, adaptive fuzzy systems are used. And to estimate the unavailable states, a simple synchronization error is constructed. To prove the stability of the overall closed-loop system (controller, observer, and drive-response system) and to design the adaptation laws, a Lyapunov theory and strictly positive real (SPR) approach are exploited. Finally, three academic examples are given to show the effectiveness of this proposed lag-synchronization scheme.
Asier Ibeas, Ali Esmaeili, Jorge Herrera, and Farouk Zouari
IEEE
This paper designs a discrete-time state-feedback output tracking control for the heart rate during treadmill exercise. Initially, the nonlinear model describing the relationship between the heart rate and the speed of a treadmill is discretized. Afterwards, a feedback linearization-based control law is proposed to achieve perfect output tracking. The control objective is to make the runner's heart rate follow a heart rate reference profile set by especialists as reference. The set-up of the problem in discrete time allows taking into consideration the effect of sampling during the controller design procedure instead of relegating it to the implementation stage. It will be shown that a linear state feedback controller is enough to make the nonlinear model's output track the reference profile regardless its possibly complex time variation. Since the full state is not available for measurement a reduced order state observer is incorporated into the discrete-time control law. Then, the continuous control command is generated by using a zero order hold (ZOH). The designed control system is tested on the original continuous-time nonlinear model by computer simulation to demonstrate the effectiveness of the proposed method to achieve the required objective.
Farouk Zouari, Kamel Ben Saad, and Mohamed Benrejeb
IEEE
In this paper, an adaptive backstepping control method is developed for a single-link robotic manipulator coupled to a brushed direct current DC motor with a nonrigid joint. The developed method uses the Lyapunov approach. It guarantees the uniform ultimate boundedness of the closed-loop system signals and the tracking error converges to zero asymptotically for any initial conditions. Simulation results also demonstrate the feasibility, effectiveness and advantage of the method.
Farouk Zouari, Kamel Ben Saad, and Mohamed Benrejeb
IEEE
This paper proposes an adaptive backstepping control method for a class of uncertain single input single output nonlinear systems. The proposed method is based on the robust stability property of the Lyapunov method. This method can ensures the uniform ultimate boundedness of the closed-loop system signals and the tracking error converges to zero for any initial conditions. Simulation results also show the effectiveness and advantage of the method.
Farouk Zouari, Kamel Ben Saad, and Mohamed Benrejeb
SAGE Publications
This paper develops a robust adaptive control for a class of nonlinear systems using the backstepping method. The proposed robust adaptive control is a recursive method based on the Lyapunov synthesis approach. It ensures that, for any initial conditions, all the signals of the closed-loop system are regularly bounded and the tracking errors converge to zero. The results are illustrated with simulation examples.