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A new LMI design technique is developed to address the problem of circle criterion-based $\mathcal{H}_{\infty}$ observer design for nonlinear systems. The developed technique applies to both locally Lipschitz as well as monotonic nonlinear systems, and allows for nonlinear functions in both the process dynamics and output equations. The LMI design condition obtained is less conservative than all previous results proposed in the literature for these classes of nonlinear systems. By judicious use of a modified Young’s relation, additional degrees of freedom are included in the observer design. These additional decision variables enable improvements in the feasibility of the obtained LMI. Several recent results in the literature are shown to be particular cases of the more general observer design methodology developed in this paper. Illustrative examples are given to show the effectiveness of the proposed methodology. The application of the method to slip angle estimation in automotive applications is discussed and experimental results are presented.

This paper investigates the problem of adaptive fuzzy tracking control for nonlinear strict-feedback systems with input delay and output constraint. Input delay is handled based on the information of Pade approximation and output constraint problem is solved by barrier Lypaunov function. Some adaptive parameters of the controller need to be updated online through considering the norm of membership function vector instead of all sub-vectors. A novel adaptive fuzzy tracking control scheme is developed to guarantee all variables of the closed-loop systems are semiglobally uniformly ultimately bounded, and the tracking error can be adjusted around the origin with a small neighborhood. The stability of the closed-loop systems is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach.

In this work, an adaptive iterative learning control scheme is proposed to deal with nonlinearly parameterized and completely non-affine pure feedback nonlinear systems. The considered systems are assumed to perform the same operation repeatedly under alignment condition. To overcome the design difficulty from non-affine structure of pure feedback system, mean value theorem is exploited to deduce affine appearance of state variables to be used as virtual controls and actual control. The nonlinearly connected parameters are separated from the local Lipschitz continuous nonlinear functions, and then iterative learning laws and adaptive iterative learning laws are designed. Lyapunov functional stability analysis method has been used to prove the stability of the closed-loop control system and the convergence of tracking error to zero as iteration goes to infinity. Simulation results are provided to illustrate the performance of the proposed scheme.

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.

In this paper, a new PSO algorithm called the duplicate particles swarm optimization (D-PSO) is used to find the optimal proportional integral (PI) controller’s gains for the control of a wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG). The control objective is the extraction of the maximum power available from the wind with zero stator reactive power regulation. Finding the optimal PI parameters consists of minimizing a cost function quantifying the mean square error between the desired output and the system output. Conducted simulation results using Simulink/Matlab confirm that the PI controller based on the proposed duplicate particle swarm optimization (D-PSO) algorithm yield much better results compared to non-optimized controllers, and conventional PSO optimized controllers.

The objective of this paper is extracting the maximum wind power from a doubly fed induction generator (DFIG) based wind energy conversion system (WECS). The DFIG model is uncertain due to the absence of total knowledge to the system’s parameters, such as inductances, resistance, and the external perturbations. Thus the need for intelligent robust controllers. In this article we propose a Backstepping controller based on Adaptive Neural Networks (ANN), the principle of the proposed strategy is that the controller uses a rejection term equivalent to the ANN estimated value of these uncertainties. The ANN weights are trained online based on an integral law, the stability of the system and the convergence of the tracking error is proven via Lyapunov theory. Simulation results confirm the ability of the ANN to estimate the uncertainties, the robustness and the efficiency of the proposed controller.

In this paper, a new fractional fuzzy adaptive backstepping control scheme is investigated for projective synchronizing a class of uncertain chaotic master-slave systems. These systems are assumed to be with fractional-order and subject to uncertainties and external disturbances. In the design process, the uncertain nonlinear functions are online modeled via fuzzy logic systems, and the virtual control terms are adequately determined based on the fractional Lyapunov stability. The proposed fuzzy adaptive backstepping controller ensures the stability of the closed-loop system as well as the convergence of the underlying synchronization error. Numerical simulations illustrate well the performances of the proposed approach.

This article considers the neural adaptive control issues of a category of non-integer-order non-square plants with actuator Nonlinearities and Asymmetric Time-Varying pseudo-State Constraints. First, the original non-square non-affine system with input nonlinearities is transformed into an equivalent affine-in-control square model by defining a set of auxiliary variables and by employing the mean-value theorem. Second, Neural networks and Nussbaum functions are exploited to obviate the requirement of a complete knowledge of the system dynamics and the control directions, respectively. Third, a novel adaptive dynamic surface control method based on Caputo fractional derivative definitions and fractional order filters is developed to overcome the “explosion of complexity” problem in the traditional backstepping design process and to determine the parameter update laws and control signals, concurrently. Then, Asymmetric Barrier Lyapunov Functions with error variables are adopted to ensure the uniform stability of the closed-loop system and to prevent the violation of the full pseudo-State constraints. The novelties and contributions of this article are: (1) through the introduction of new technical Lemmas and corollaries, existing control design and stability theories linked to integer-order square systems are developed and extended to non-square non-integer-order ones. (2) all signals, including variables and errors in the closed-loop system are semi-global practical finite-time stability whereas the the tracking errors are asymptotically driven to zero without transgression of the constraints. Finally, the effectiveness and potential of the proposed control approach are substantiated by two example simulations.

The various civilizations which followed one another on the Algerian territory marked it by their presence. The architectural and urban production of each period displays its concepts and its styles, each rich with specific characteristics. The most representative buildings date to the Ottoman period and especially to French colonization. During the latter, and with the multiplication of the administrative offices, numerous public buildings represent different styles: Neoclassical, eclectic, neo-Moorish and art deco, each lying next to one another. Nevertheless, each building was intended to indicate its function. Numerous studies of the buildings erected during the first half of the twentieth century and particularly on the attributes of the neo-Moorish style were examined unlike the architectural output resulting from the Second Empire and relating to public civil architecture, due to the lack of monographic work. Our knowledge in this field thus remains to be improved and constituted. This paper is devoted to the town hall of Annaba, through which we will illustrate the attributes and architectonic specificities as well as the codes inherent in French public civil architecture in Algeria.

In presence of the uncertainties in robot dynamics, model-based control law can easily fail. In our paper, this important question is tackled by using fuzzy adaptive control to drive with obstacle avoidance an industrial redundant manipulator under the hypothesis of uncertain dynamics. This hypothesis makes our controller an original one, since the problem of the tracking trajectory in the presence of obstacles and assuming that the robot dynamics are unknown has not been discussed before in the literature. Besides, the proposed obstacle avoidance is achieved by generating a self-motion. Furthermore, the usual procedure related to the obstacle avoidance based on Euclidean distance in 3D space is made easier since this one includes only the parameters of 2D space. This self-motion is directly incorporated into the adaptive fuzzy control scheme via the filtered tracking errors. The proposed method is tested by simulations in the case of redundant robot (PUMA 560) operating in 3D space in presence of obstacles. Despite that the robot dynamics are assumed to be uncertain, the proposed control performance is satisfactory. Indeed, the trajectory tracking control, incorporating self-motion obstacle avoidance, operates effectively with weak tracking control errors and bounded actuator torques evolving in admissible range.

This paper focuses on the adaptive neural output feedback control of a class of uncertain multi-input–multi-output nonlinear time-delay non-integer-order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. Thus, the mean value theorem and a Gaussian error function-based continuous differentiable model are used in the paper to describe the unknown asymmetric saturation actuator and to get an affine model in which the control input appears in a linear fashion, respectively. The design of the controller follows a number of steps. Firstly, based on the semigroup 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 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. The main advantages of this work are that: (1) it can handle systems with constant, time-varying, and distributed time-varying delays, (2) the considered class of systems is relatively large, (3) the number of adjustable parameters is reduced, (4) the tracking errors converge asymptotically to zero and all signals of the closed-loop system are bounded. Finally, some simulation examples are provided to demonstrate the validity and effectiveness of the proposed scheme.

The article presents an adaptive fuzzy control approach to the problem of control of electrostatically actuated MEMS, which is based on differential flatness theory and which uses exclusively output feedback. It is shown that the model of the electrostatically actuated MEMS is a differentially flat one and this permits to transform it to the so-called linear canonical form. For the new description of the system's dynamics the transformed control inputs contain unknown terms which depend on the system's parameters. To identify these terms an adaptive fuzzy approximator is used in the control loop. Thus an adaptive fuzzy control scheme is implemented in which the unknown or unmodeled system dynamics is approximated by neurofuzzy networks and next this information is used by a feedback controller that makes the electrostatically activated MEMS converge to the desirable motion setpoints. This adaptive control scheme is exclusively implemented with the use of output feedback, while the state vector elements which are not directly measured are estimated with the use of a state observer that operates in the control loop. The learning rate of the adaptive fuzzy system is suitably computed from Lyapunov analysis, so as to assure that both the learning procedure for the unknown system's parameters, the dynamics of the observer and the dynamics of the control loop will remain stable. The Lyapunov stability analysis depends on two Riccati equations, one associated with the feedback controller and one associated with the state observer. Finally, it is proven that for the control scheme that comprises the feedback controller, the state observer and the neurofuzzy approximator, an H-infinity tracking performance can be achieved. The functioning of the control loop has been evaluated through simulation experiments.

In this paper, a novel adaptive fuzzy variable-structure control approach is proposed for a class of multivariable nonaffine uncertain systems subject to dynamic nonlinear disturbances and actuator nonlinearities (dead zone and sector nonlinearities). By using the Taylor series expansion, an equivalent model in affine-like form is first derived for the nonaffine system. Then after, an adaptive fuzzy variable-structure controller is designed based on this affine-like equivalent model. The adaptive fuzzy systems are used to appropriately approximate the unknown nonlinear functions. The so-called SDU decomposition of the control gain matrix is exploited in the controller design and the stability analysis. It is proved that the proposed adaptive fuzzy control scheme can guarantee that all involved signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are presented to illustrate the effectiveness of this adaptive control system.

In this paper, a modified generalized function projective synchronization scheme for a class of master–slave chaotic systems subject to dynamic disturbances and input nonlinearities (dead-zone and sector nonlinearities) is investigated. This synchronization system can be seen as a generalization of many existing projective synchronization schemes (namely the function projective synchronization, the modified projective synchronization and so on), in the sense that the master system has a scaling function matrix and the slave system has a scaling factor matrix. To practically achieve this generalized function synchronization, an adaptive fuzzy variable-structure control system is designed. The fuzzy systems are used to appropriately approximate the uncertain nonlinear functions. A Lyapunov approach is employed to prove the boundedness of all signals of the closed-loop control system as well as the exponential convergence of the synchronization errors to an adjustable region. Simulations results are ...

This paper proposes a novel fuzzy adaptive controller for achieving an appropriate generalized projective synchronization (GPS) of two incommensurate fractional-order chaotic systems. The master system and the slave system, considered here, are assumed to be with non-identical structure, external dynamical disturbances, uncertain models and distinct fractional-orders. The adaptive fuzzy systems are used for estimating some unknown nonlinear functions. A Lyapunov approach is adopted for deriving the parameter adaptation laws and proving the stability of the closed-loop system. Under some mild assumptions, the proposed controller can guarantee all the signals in the closed-loop system remain bounded and the underlying synchronization errors asymptotically converge towards a small of neighborhood of the origin. Finally, some numerical experiment results are presented to illustrate the effectiveness of the proposed synchronization scheme.

This research work addresses the fuzzy adaptive controller design for a generalized projective synchronization (GPS) of incommensurate fractional-order chaotic systems with input nonlinearities. The considered master-slave systems are with different fractional-orders, uncertain models, unknown bounded disturbances and non-identical form. The suggested controller includes two main terms, namely, a fuzzy adaptive control and a fractional-order variable structure control. The fuzzy logic systems are exploited for approximating the system uncertainties. A Lyapunov approach is employed for determining the parameter adaptation laws and proving the stability of the closed-loop system. At last, simulation results are given to demonstrate the validity of the proposed GPS approach.

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 frame-work. 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 adaptive laws, a Lyapunov theory and strictly positive real (SPR) approach are exploited.

Although extensive research has been conducted to design path-following algorithms for automated vehicles, the cross comparison between different path-following controllers is still weakly-analyzed. Therefore, we benchmarked five path-following algorithms to evaluate their performance according to various disturbances like gust wind, drop of road friction coefficient and inaccurate GPS localization. The comparison was carried out in simulation environment between geometrical-based, path controller with preview, LQR, linear MPC and observer-based controller with integral action approaches.

This chapter addresses the fuzzy adaptive controller design for the generalized projective synchronization (GPS) of incommensurate fractional-order chaotic systems with actuator nonlinearities. The considered master-slave systems are with different fractional-orders, uncertain models, unknown bounded disturbances, and non-identical form. The suggested controller includes two keys terms, namely a fuzzy adaptive control and a fractional-order variable structure control. The fuzzy logic systems are exploited for approximating the system uncertainties. A Lyapunov approach is employed for determining the parameter adaptation laws and proving the stability of the closed-loop system. At last, simulation results are given to demonstrate the validity of the proposed synchronization approach.

International audience; This study deals with a fault sensor estimation and state variables estimation based on Unknown Input Observer for automated steering vehicle. The vehicle lateral dynamics is represented by a fourth degree of freedom model. This nonlinear model is transformed into linear parameter varying model where the longitudinal velocity is considered as parameter varying. Then, an Unknown Input Observer is designed in order to reconstruct the states variables in presence of sensor faults. Based on Lyapunov theory, the observer gains are computed using Linear Matrix Inequalities. Simulation results are given to show the effectiveness of the proposed approach to detect sensor faults and to discriminate the disturbance.

International audience; A new LMI design technique is developed to address the problem of circle criterion-based $\mathcal{H}_{\infty}$ observer design for nonlinear systems. The developed technique applies to both locally Lipschitz as well as monotonic nonlinear systems, and allows for nonlinear functions in both the process dynamics and output equations. The LMI design condition obtained is less conservative than all previous results proposed in the literature for these classes of nonlinear systems. By judicious use of a modified Young’s relation, additional degrees of freedom are included in the observer design. These additional decision variables enable improvements in the feasibility of the obtained LMI. Several recent results in the literature are shown to be particular cases of the more general observer design methodology developed in this paper. Illustrative examples are given to show the effectiveness of the proposed methodology. The application of the method to slip angle estimation in automotive applications is discussed and experimental results are presented.

In this paper, the problem of designing a new iterative learning control has been investigated for a class of strict-feedback nonlinear systems subject to both structured and unstructured uncertainties and dynamic disturbances. The considered systems are assumed to perform the same operation repeatedly under alignment condition. Simple learning mechanisms are proposed to estimate the unknown state-dependent nonlinear functions satisfying local Lipschitz conditions. By using the concept of command filtered backstepping, the problem of the explosion of complexity existing in conventional backstepping is eliminated and the proposed controller is greatly simplified. Lyapunov-like functional method is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iterations. Simulation results are provided to show the effectiveness of the proposed control scheme.

This paper deals with the issue of projective synchronization of two distinct fractional-order chaotic systems with the presence of both uncertain dynamics and external disturbances. More precisely, this study is an attempt to investigate a novel fuzzy adaptive controller for achieving an appropriate projective synchronization of uncertain fractional-order chaotic systems. The adaptive fuzzy systems are utilized to online estimate unknown system nonlinearities. The proposed controller, which is derived based on a Lyapunov approach, is continuous and ensures the stability of the closed-loop system and the exponential convergence of the underlying synchronization errors to a small residual set. Finally, three simulation examples are provided to verify the effectiveness of the proposed synchronization method.

In this paper, the projective synchronization problem of two fractional-order different chaotic (or hyperchaotic) systems with both uncertain dynamics and external disturbances is considered. More particularly, a fuzzy adaptive control system is investigated for achieving an appropriate projective synchronization of unknown fractional-order chaotic systems. The adaptive fuzzy logic systems are used to approximate some uncertain nonlinear functions appearing in the system model. These latter are augmented by a robust control term to compensate for the unavoidable fuzzy approximation errors and external disturbances as well as residual error due to the use of the so-called e-modification in the adaptive laws. A Lyapunov approach is adopted for the design of the parameter adaptation laws and the proof of the corresponding stability as well as the asymptotic convergence of the underlying synchronization errors towards zero. The effectiveness of the proposed synchronization system is illustrated through numerical experiment results.

This paper presents a new adaptive fuzzy vector controller (AFVC) to handle the torque and speed tracking problem of a doubly-fed induction motor (DFIM) as an alternative to classical PI based vector control method generally used for its simplicity. However, the control performance of DFIM is still influenced by the variations of the parameters, the external load disturbances and perturbations in practical applications. Then, it is difficult to achieve high control performances of DFIM by using conventional PI-type control techniques. The proposed AFVC scheme uses adaptive fuzzy systems to reasonably approximate the uncertain dynamics appearing in the DFIM, relaxing thereby the usual modeling requirement about the DFIM dynamics. Of fundamental interest, it is shown that all the closed-loop signals are bounded and the tracking errors exponentially converge to a residual set. Probing simulation results are given to emphasize the effectiveness of the proposed AFVC system with respect to the usual feedback linearization based vector control (FLVC) system.

This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples.

This research fixed as objective the study and the analysis of buildings belonging to the colonial period. This buildings important damages today, whether it is by the custom or under the influence of time. So and considering their importances in the national history and their values of architectural heritage; we attempted to develop a method of analysis susceptible to help in their long-lasting identification. The area of this study was the city center of Annaba, which reflects a typology recognized through the majority of the Algerian big cities. The developed approach consists in making a detailed reading of the various architectural and architectonic elements composing the colonial facade. The end, we were able to identify the basic typologies, which can serve as references for the composition of a facade of the XIXth and XXth century to Annaba. Beyond this receipt, our work could be of use to future operations of rehabilitation, renovation, and requalification or of protection of this type of construction. Also this architecture would inspire contemporary realizations.

This paper presents a secure image transmission based on a chaotic synchronization. Chaotic systems present unpredictable and complex behavior. These characteristics, together with the dependence on the initial conditions as well as the tolerance of this type of systems, make theme ideal for transmission. In this paper a system of encryption based on chaotic signal is proposed. This system is used for the purpose of secure image transmission. For this purpose an algorithm for the encryption and the decryption process is proposed. In the proposed system, the transmitter mixes an input image with a chaotic signal at transmitter level using masking technique and a mixture function. The receiver must be able to reproduce exactly the same chaotic signal in order to subtract it from the received signal. This becomes possible with synchronization between the transmitter and the receiver: through the use of specific techniques, the trajectory of the Slave chaotic system can be bound to that of the Master system producing (almost) identical behavior. Simulation results demonstrate satisfactory performance, and robust against common attacks. The whole system is simulated in Matlab. The system works with both greyscale and color images.

In this chapter, one investigates the chaos synchronization of a class of uncertain optical chaotic systems. More precisely, one also presents a systematic approach for designing a fractional-order (FO) sliding mode controller to achieve a rapid, robust, and perfect chaos synchronization. By this robust controller, it is rigorously proven that the associated synchronization error is Mittag-Leffler (or asymptotically) stable. In a numerical simulation framework, this synchronization scheme is tested on many chaotic optical systems taken from the open literature. The obtained results clearly show that the proposed chaos synchronization controller is not only strongly robust with respect to the unavoidable system's uncertainties (as unmodeled dynamics, and parameters' variation and uncertainty) and eventual dynamical external disturbances, but also can significantly reduce the chattering effect.

A l’instar des villes Algériennes, après la dissolution de sa Régie Municipale RMCA, la ville d’Annaba, n’a pas échappé aux effets négatifs générés par la libéralisation du secteur de transport impulsé par la loi n° 88-17 du 10 mai 1988 portant orientation et organisation des transports terrestres, et dont ses effets qualifiables de «stigmates» demeurent à ce jour.Cet article se penche sur l’opération de libéralisation ordonnée par l’état pendant le début des Années 90, en proposant un phasage chronologique relatif à son évolution, tout en mettant la lumière sur ses effets les plus manifestes à l’échelle nationale et surtout sur le plan urbain par rapport au périmètre de transport de la ville d’Annaba, ceci à travers ses différents volets organisationnels et fonctionnels.Mots clés: Libéralisation - Transport public - Chronologie - Urbain - OrganisationEnglish Title: Liberalization as a source of deregulation of public transport in Annaba cityEnglish AbstractSimilarly at almost of the Algerian cities, after the dissolution of the Municipal Board RMCA, the city of Annaba doesn’t escape from the negative effects generated by the liberalization of the transport sector; Promoted by Law 88-17 of May 10th,1988, concerning the orientation and organization of land transportation, including its effects classifiable “stigmata”, who persists until the present .This work examines the orderly liberalization process by the state during the early 90's, offering a chronology of its evolution, while shedding light on its most obvious effects at different levels, nationally and especially in urban scale compared to PUT perimeter of urban transport of Annaba city, also through its various organizational and functional aspects.Keywords: Liberalization - Public transport - Chronology - Urban - Organization