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International audience; For joint estimation of states and parameters in time varying time-delay systems (TDS) involving both distributed and lumped time-delays, a general approach is proposed in this paper to transforming existing (non adaptive) observers to adaptive observers. In addition to the convergence conditions of the considered existing observers, a persistent excitation condition is introduced in order to ensure the convergence of parameter estimation. In contrast to implicitly formulated convergence conditions, which are usually assumed jointly for both state and parameter estimations in most TDS adaptive observers, the persistent excitation condition in the proposed approach is explicitly formulated and decoupled from the conditions initially assumed for state estimation.

The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation (PE) conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

International audience; Adaptive observers are recursive algorithms for joint estimation of both state variables and unknown parameters. Usually some persistent excitation (PE) condition is required for the convergence of adaptive observers. However, in practice, it may happen that the PE condition is not satisfied, because the available sensor signals do not contain sufficient information for the considered recursive estimation problem, which is ill-posed. To remedy the lack of PE condition, inspired by typical methods for solving ill-posed inverse problems, this paper proposes a regularized adaptive observer for general linear time varying (LTV) systems. Two regularization terms are introduced in both state and parameter estimation recursions, in order to preserve the state-parameter decoupling transformation involved in the design of the adaptive observer. Like in typical ill-posed inverse problems, regularization implies an estimation bias, which can be reduced by using prior knowledge about the unknown parameters.

We present a new approach for deriving sampled-data observers from continuous-time observers that feature an Input-to-Output Stability property with respect to the output measurement noise and exponential convergence in the noiseless case. The design approach applies to a wide class of systems and yields sampled-data observers that inherit all performance characteristics of the underlying continuous-time observers. The main component of the proposed sampled-data observer is a novel output predictor that encompasses both inter-sample predictors and ZOH-predictors., 14 pages, 1 figure, submitted to IEEE TAC for possible publication

International audience; The design of challenging and ambitious space missions entails a tightening of spacecraft pointing constraints. Among the many perturbations that have to be addressed, the sloshing of the on-board propellant is a complex issue. Recent developments in Computational Fluid Dynamics, supported by in-situ experiments like Fluidics or Sloshsat-FLEVO, open up ways for the characterization of liquids behavior inside tanks in microgravity. This knowledge can be applied in the context of spacecraft modeling and control. For this purpose, we present here a new approach to model the disruptive sloshing dynamics affecting spacecraft during attitude maneuvers. With the aim of mitigating slosh effects, this model will be used to design robust attitude controllers.

We design an observer for ODE-PDE cascades where the ODE is nonlinear of strict-feedback structure and the PDE is a linear and of parabolic type. The observer provides online estimates of the (finite-dimensional) ODE state vector and the (infinite-dimensional) state of the PDE, based only on sampled boundary measurements. A design that simultaneously addresses nonlinear ODEs and boundary measurement sampling is the paper’s key contribution. Our observer design combines the backstepping design approach and the high-gain observer methodology and our analysis employs a Lyapunov–Krasovskii type functional to establish exponential convergence. Our sufficient conditions for convergence involve the maximum sampling interval and the PDE domain length.

The present work provides a systematic approach for the design of sampled-data observers to a wide class of 1-D, parabolic PDEs with non-local outputs. The studied class of parabolic PDEs allows the presence of globally Lipschitz nonlinear and non-local terms in the PDE. Two different sampled-data observers are presented: one with an inter-sample predictor for the unavailable continuous measurement signal and one without an inter-sample predictor. Explicit conditions on the upper diameter of the (uncertain) sampling schedule for both designs are derived for exponential convergence of the observer error to zero in the absence of measurement noise and modeling errors. Moreover, explicit estimates of the convergence rate can be deduced based on the knowledge of the upper diameter of the sampling schedule. When measurement noise and/or modeling errors are present, Input-to-Output Stability (IOS) estimates of the observer error hold for both designs with respect to noise and modeling errors. The main results are illustrated by two examples which show how the proposed methodology can be extended to other cases (e.g., boundary point measurements)., 20 pages, submitted to Systems and Control Letters for possible publication

International audience; The problem of observer design is addressed for a class of triangular nonlinear systems with not-necessarily small delay and sampled output measurements. One more difficulty is that the system state matrix is dependent on the un-delayed output signal which is not accessible to measurement, making existing observers inapplicable. A new chain observer, composed of m elementary observers in series, is designed to compensate for output sampling and arbitrary large delays. The larger the time-delay the larger the number m. Each elementary observer includes an output predictor that is conceived to compensate for the effects of output sampling and a fractional delay. The predictors are defined by first-order ordinary differential equations (ODEs) much simpler than those of existing predictors which involve both output and state predictors. Using a small gain type analysis, sufficient conditions for the observer to be exponentially convergent are established in terms of the minimal number m of elementary observers and the maximum sampling interval.

International audience; The problem of state and parameter estimation is addressed for systems with cascade structure including finite-dimensional dynamics followed in series with infinite-dimensional dynamics. The formers are captured through an ODE that is state- and parameter-affine. The latter, referred to as sensor dynamics, are represented by a diffusion parabolic PDE. Both equations are subject to parameter uncertainty. Furthermore, the connection point between the ODE and the PDE blocs is not accessible to measurements. The aim is to get online estimates of all inaccessible states and unknown parameters of both the ODE and the PDE subsystems. This observation problem is dealt with by combining the backstepping design method and the extended Kalman observer approach. The obtained adaptive observer is shown to be exponentially convergent under an ad-hoc persistent excitation condition.

International audience; A small-gain approach is presented for analyzing exponential stability of a class of (dynamical) hybrid systems. The systems considered in the paper are composed of finite-dimensional dynamics, represented by a linear ordinary differential equation (ODE), and infinite-dimensional dynamics described by a parabolic partial differential equation (PDE). Exponential stability is established under conditions involving the maximum allowable sampling period (MASP). This new stability result is shown to be useful in the design of sampled-output exponentially convergent observers for linear systems that are described by an ODE-PDE cascade. The new stability result also proves to be useful in designing practical approximate observers involving no PDEs.

International audience; The problem of observer design is addressed for a class of triangular nonlinear systems in presence of output measurement sampling and time-delay. A major difficulty with the considered nonlinear systems is that the state matrix is dependent on the "undelayed output signal" which is not accessible to measurement, making existing observers inapplicable. A new observer is designed where the effects of time-delay and sampling are compensated for using an output predictor. Defined by a couple of first-order ordinary differential equations (ODEs), the present predictor turns out to be much simpler compared to previous predictors involving output and state predictors. Using the small gain technique, sufficient conditions for the observer to be exponentially convergent are established in terms of the maximum time-delay and sampling interval.

International audience; We are considering the problem of state observation for a class of infinite dimensional systems modeled by parabolic type PDEs. The model is subject to parametric uncertainty entering in both the domain equation and the boundary condition. An adaptive boundary observer, providing online estimates of the system state and parameters, is designed using finite- and infinite-dimensional backstepping-like transformations. The observer is exponentially convergent under an ad hoc persistent excitation condition.

International audience; The problem of state observation is addressed for a class of systems subject to sensor delay and parameter uncertainty. The unknown parameter vector enter a finite-dimensional state equation through a possibly output-dependent regressor. The sensor delay effect is captured by a first-order hyperbolic PDE. Doing so, the system turns out to be an ODE-PDE association with a connection point not accessible to measurements. An adaptive observer is constructed by combining ideas from PDE-based and ODE-based design approaches. The observer provides estimates of the ODE subsystem states and parameters, on the one hand, and of the sensor states, on the other. Observer exponential convergence is established under an ad-hoc persistent excitation condition involving the regressor.

International audience; This paper studies the application of a recently proposed control scheme to globally Lipschitz nonlinear systems for which the input is delayed and applied with zero order hold, the measurements are sampled and delayed, and only an output is measured (i.e., the state vector is not available). The control scheme consists of an observer for the delayed state vector, an inter-sample predictor for the output signal, an approximate predictor for the future value of the state vector, and the nominal feedback law applied with zero order hold and computed for the predicted value of the future state vector. The resulting closed-loop system is robust with respect to modeling and measurement errors and robust to perturbations of the sampling schedule.

International audience; This paper provides exponential stability results for two system classes. The first class includes a family of nonlinear ODE systems while the second consists of semi-linear parabolic PDEs. A common feature of both classes is that the systems they include involve sampled-data states and a time-varying gain. Sufficient conditions ensuring global exponential stability are established in terms of Linear Matrix Inequalities (LMIs) derived on the basis of Lyapunov–Krasovskii functionals. The established stability results prove to be useful in designing exponentially convergent observers based on sampled-data measurements. It is shown throughout simulated examples from the literature that the introduction of time-varying gains is beneficial to the enlargement of sampling intervals while preserving the stability of the system.

International audience; The problem of state observation, based on spatially-sampled output measurements, is addressed for a class of infinite dimensional systems, modelled by a semi-linear heat equation augmented with a structured uncertain part involving a set of unknown parameters. An adaptive observer is designed that provides online estimates of the system (spatially distributed) state and unknown parameters based on sampled data (in space). Sufficient conditions for the observer to be exponentially convergent are established. These include an ad-hoc persistent excitation condition as well as a condition on how the observer gain must be selected in relation with the space sampling interval.

International audience; The problem of state-observation is addressed for nonlinear systems that can be modeled by an ODE-PDE series association. The ODE subsystem assumes a triangular structure while the PDE element is of heat diffusion type. The aim is to accurately estimate online the state vector of the ODE subsystem and the distributed state of the PDE element. One major difficulty is that the state observation must only rely on the global system output i.e. the PDE state at the terminal boundary. In particular, the connection point between the ODE and the PDE blocks is not accessible to measurements. The observation problem is dealt with by designing a high-gain type observer. Sufficient conditions involving the PDE domain length are formally established that ensure the observer exponential convergence. (C) 2015 Elsevier B.V. All rights reserved.

International audience; This paper provides exponential stability results for a family of nonlinear ODE systems which involves sampled-data states and a time-varying gain. Sufficient conditions ensuring global exponential stability are established in terms of Linear Matrix Inequalities (LMIs) derived on the basis of Lyapunov-Krasvoskii functionals. The established stability results prove to be useful in designing exponentially convergent observers based on the sampled-data measurements. It is shown throughout simple examples from the literature that the introduction of time-varying gains is quite beneficial for the enlargement of sampling intervals while preserving the stability of the system.

International audience; We are considering the problem of state observation for a class of infinite dimensional systems. The latter are modelled by a parabolic PDE and a boundary condition involving an unknown parameter. The problem is dealt with using an adaptive observer that provides online estimates of the system (spatially distributed) state and the unknown parameters. The observer is formally shown to be exponentially convergent under an ad-hoc persistent excitation condition.

International audience; This article addresses the problem of controlling DC-DC interleaved boost converters associated to fuel cell energy generators. The fuel cell–interleaved boost converter association powers a possibly unknown and time-varying resistance load; furthermore, interleaved boost converter parallel cells are not equipped with current sensors. The control objective is twofold: (1) the interleaved boost converter output voltage must be tightly regulated and (2) the total current carried by the interleaved boost converter must be equally shared between the different parallel branches. The complexity of the control problem lies in (i) the system non-linearity and instability of system zero dynamics with respect to output voltage, (ii) load uncertainty, and (iii) inaccessibility to measurements of all currents. The instability of the output voltage zero dynamics is addressed by reformulating all control objectives in terms of current regulation in the different converter cells. The resulting current regulation problem is dealt with by developing an adaptive output feedback controller including a collection of adaptive current regulators and estimators. Parameter adaptation is used for compensation of load uncertainty. It is shown that the proposed output feedback adaptive controller meets its objectives. This theoretical analysis is confirmed by numerical simulations and experimental tests showing additional robustness features.

International audience; In this paper we propose a new finite-time state observer for a nonlinear space launch vehicle with flexible dynamics and uncertain parameters. Indeed, flexible states are required to ensure nonlinear control objectives of both reference path tracking and bending mode damping. Our main contribution is to show that it is enough to observe a sublinear uncertain system to ensure a finite-time convergence of the whole state. For that purpose, a Luenberger observer is mixed with a parameter and initial state estimator, based on algebraic estimation tools. Closed loop simulations show the effectiveness of the observer in combination with a backstepping control design (extended to flexible systems).

International audience; Predictor-based stabilization results are provided for nonlinear systems with input delays and a compact absorbing set. The control scheme consists of an inter-sample predictor, a global observer, an approximate predictor, and a nominal controller for the delay-free case. The control scheme is applicable even to the case where the measurement is sampled and possibly delayed. The closed-loop system is shown to have the properties of global asymptotic stability and exponential convergence in the disturbance-free case, robustness with respect to perturbations of the sampling schedule, and robustness with respect to measurement errors. In contrast to existing predictor feedback laws, the proposed control scheme utilizes an approximate predictor of a dynamic type which is expressed by a system described by Integral Delay Equations. Additional results are provided for systems that can be transformed to systems with a compact absorbing set by means of a preliminary predictor feedback.

International audience; New strategies are proposed to design GAS controllers for two significant classes of nonlinear flexible systems. Inspired by a non affine control technique, we have designed some novel nonlinear control laws which use the full state in order to enhance the damping of the flexible modes: this new design is all the more interesting than the classes of considered systems belong to non triangular nonlinear systems. Finally, the effectiveness of our approach is illustrated on simplified models of a flexible launcher and spacecraft.

International audience; This chapter presents two observer designs for nonlinear systems with sampled measurements and their application to an induction motor. The first design is a direct adaptation of the continuous high gain observer. The output is sampled and its value is held, thus the correction term in the observer equation is constant between sampling instants. The second observer scheme includes a predictor based on the work by Karafyllis and Kravaris to estimate the output between two sampling instants. At each sampling instant, the predictor is reset to the actual value of the output. The output estimation is fed to the correction term of the observer. The Matlab/Simulink is used to test the implementation of those observers on a simulation model of the induction machine.

This paper presents new results concerning the observer design for wide classes of nonlinear systems with both sampled and delayed measurements. By using a small gain approach we provide sufficient conditions, which involve both the delay and the sampling period, ensuring exponential convergence of the observer system error. The proposed observer is robust with respect to measurement errors and perturbations of the sampling schedule. Moreover, new results on the robust global exponential state predictor design problem are provided, for wide classes of nonlinear systems., 17 pages, submitted for possible publication to Systems and Control Letters

International audience; This paper deals with power extraction maximization of a doubly fed induction generator (DFIG)-based wind turbine. These variable speed systems have several advantages over the traditional wind turbine operating methods, such as the reduction of the mechanical stress and an increase in the energy capture. To fully exploit this latest advantage, many control schemes have been developed for maximum power point tracking (MPPT) control schemes. In this context, this paper proposes a second-order sliding mode to control the wind turbine DFIG according to references given by an MPPT. Traditionally, the desired DFIG torque is tracked using control currents. However, the estimations used to define current references drive some inaccuracies mainly leading to nonoptimal power extraction.Therefore, using robust control, such as the second-order sliding mode, will allow one to directly track the DFIG torque leading tomaximum power extraction.Moreover, the proposed control strategy presents attractive features such as chattering-free behavior (no extra mechanical stress), finite reaching time, and robustness with respect to external disturbances (grid) and unmodeled dynamics (generator and turbine). Simulations using the wind turbine simulator FAST and experiments on a 7.5-kW real-time simulator are carried out for the validation of the proposed high-order sliding mode control approach.

International audience; This work presents a simple observer for triangular nonlinear systems with time varying delayed output measurement. A sufficient condition ensuring the asymptotic convergence of the observation error towards zero is given, as well as an explicit relation between the bound of the delay and the parameter of the observer. This result is illustrated by some simulations.

International audience; Recently, new results about semi-global and global finite-time observation have been obtained by the use of continuous high gain observers. In this paper, we propose to extend these results by studying “time-varying high gain” observers and by providing new update laws: first, we adapt the law introduced by L. Praly in the case of asymptotic observation to the finite-time case and we prove that the updated high gain remains bounded. Secondly, we propose a new update law which guarantees the high gain's value tends asymptotically to 1.

International audience; New results on high-gain observer design for sampled-data systems are presented for a large class of nonlinear uniformly observable systems. It is shown that by using an appropriate Lyapunov function, it is possible to design a continuous high-gain observer coupled with a large class of output predictors. An explicit bound on the maximum allowable sampling interval guaranteeing stability of the observation error is given. This result is illustrated on an experimental setup.

This work was supported in part by Brest Métropole Océane (BMO), in part by the European Social Fund (ESF), and in part by the GDR SEEDS CNRS 2994 under the Internal Project HYDROLE; International audience; This paper deals with the experimental validation of a Matlab-Simulink simulation tool of marine current turbine (MCT) systems. The developed simulator is intended to be used as a sizing and site evaluation tool for MCT installations. For that purpose, the simulator is evaluated within the context of speed control of a permanent magnet synchronous generatorbased (PMSG) MCT. To increase the generated power, and therefore the efficiency of an MCT, a nonlinear controller has been proposed. PMSG has been already considered for similar applications, particularly wind turbine systems using mainly PI controllers. However, such kinds of controllers do not adequately handle some of tidal resource characteristics such as turbulence and swell effects. Moreover, PMSG parameter variations should be accounted for. Therefore, a robust nonlinear control strategy, namely second-order sliding mode control, is proposed. The proposed control strategy is inserted in the simulator that accounts for the resource and the marine turbine models. Simulations using tidal current data from Raz de Sein (Brittany, France) and experiments on a 7.5-kW real-time simulator are carried out for the validation of the simulator.

International audience; New results on high gain observer design for networked control systems via an emulation-like approach are presented. By using a general framework and a Lyapunov approach, we derive some explicit conditions on the maximum allowable transmission interval that ensure an exponential convergence of the observation error for a large class of network protocols.

International audience; This paper deals with the speed control of a variable speed doubly-fed induction generator (DFIG)-based marine current turbine (MCT). To increase the generated power and therefore the efficiency of an MCT, a nonlinear controller has been proposed. DFIG has been already considered for similar applications, particularly wind turbine systems using mainly proportional-integral (PI) controllers. However, such kinds of controllers do not adequately handle some tidal resource characteristics such as turbulence and swell effects. These may decrease MCT performances. Moreover, DFIG parameter variations should be accounted for. Therefore, a robust nonlinear control strategy, namely high-order sliding mode (HOSM) control, is proposed. This control strategy relies on the resource and the marine turbine models that were validated by experimental data. The sensitivity of the proposed control strategy is analyzed regarding the swell effect as it is considered as the most disturbing one for the resource model. Tidal current data from the Raz de Sein (Brittany, France) are used to run simulations of a 7.5-kW prototype over various flow regimes. Simulation results are presented and fully analyzed.

International audience; This paper deals with the power generation control in variable-speed wind turbines. These systems have two operation regions which depend on wind turbine tip speed ratio. A high-order sliding-mode control strategy is then proposed to ensure stability in both operation regions and to impose the ideal feedback control solution in spite of model uncertainties. This control strategy presents attractive features such as robustness to parametric uncertainties of the turbine. The proposed slidingmode control approach has been validated on a 1.5-MW threeblade wind turbine using the National Renewable Energy Laboratory wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. Validation results show that the proposed control strategy is effective in terms of power regulation.Moreover, the sliding-mode approach is arranged so as to produce no chattering in the generated torque that could lead to increased mechanical stress because of strong torque variations.

International audience; Stabilization of uncertain sampled-data strict-feedback systems is addressed. The stability study is carried out on the Euler approximation of the exact discretized model of the plant. Firstly, a class of state-feedback controllers is developed that guarantees an input-to-state stability property for the closed-loop system. Additionally, assuming some hypotheses on the uncertain terms hold, a practical asymptotic stability property is ensured by designing an appropriate class of controllers.

Assuming a class of continuous-time observers known for a given nonlinear system, an observation structure is derived when sensors information is subject to network-induced communication constraints. The approach is based on the recent results on hybrid observers for sampled-data systems by Karafyllis and Kravaris [1]. Considering two types of protocols, some asymptotic stability properties are shown to hold for the observation error under some conditions and an explicit bound on the maximum allowable transmission interval between transmission times is given. Results are then applied to the observation of several marine vessels when measures are transmitted over a shared network.

International audience; In this paper, an algorithm for direct speed and flux adaptive control of induction motors using unknown time-varying rotor resistance and load torque is described and validated with experimental results. This method is based on the variable structure theories and is potentially useful for adjusting online the induction motor controller unknown parameters (load torque and rotor resistance). The presented nonlinear compensator provides voltage inputs on the basis of rotor speed and stator current measurements, and generates estimates for both the unknown parameters and the nonmeasurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Experiments show that the proposed method achieved very good tracking performance within a wide range of the operation of the induction motor with online variation of the rotor resistance: up to (87%). This high tracking performance of the rotor resistance variation demonstrates that the proposed adaptive control is beneficial for motor efficiency. The proposed algorithm also presented high decoupling performance and very interesting robustness properties with respect to the variation of the stator resistance (up to 100%), measurement noise, modeling errors, discretization effects, and parameter uncertainties (e.g., inaccuracies on motor inductance values). The other interesting feature of the proposed method is that it is simple and easily implementable in real time. Comparative results have shown that the proposed adaptive control decouples speed and flux tracking while standard field-oriented control does not.

In this paper, a new method for the identification of nonlinear systems with time-varying parameters using a sliding-neural network observer is investigated. The proof of the finite-time convergence of the estimates to their true values is achieved using Lyapunov arguments and sliding mode theories. An application example illustrated the effectiveness of the approach and the obtained results show high convergence rate and very satisfactory parameter estimation accuracy. The computing results under noisy condition also demonstrate that good state and parameter estimation can be achieved despite the disturbance (noise) in the system. The reduced number of hidden units and the small transient period demonstrate that the proposed method can be easily implementable in real-time.

This work is supported by Brest Métropole Océane (BMO) and the European Social Fund (ESF). It is also supported by the GDR SEEDS CNRS N°2994 under the Internal Project HYDROLE. It is done within the framework of the Marine Renewable Energy Commission of the Brittany Maritime Cluster (Pôle Mer Bretagne).; International audience; This paper deals with the modeling and the sensorless control of a variable speed DFIG-based marine current turbine. The proposed MPPT sensorless control strategy relies on the resource and the marine turbine models that were validated by experimental data. The sensitivity of the proposed control strategy is analyzed regarding the swell effect as it is considered as the most disturbing one for the resource model. Tidal current data from the Raz de Sein (Brittany, France) are used to run simulations of a 7.5-kW prototype over various flow regimes. Simulation results are presented and fully analyzed.

International audience; This paper addresses the problem of controlling power generation in variable-speed wind energy conversion systems (VS-WECS). These systems have two operation regions depending on thewind turbine tip-speed ratio. They are distinguished by minimum phase behavior in one of these regions and a nonminimum phase in the other one. A sliding mode control strategy is then proposed to ensure stability in both operation regions and to impose the ideal feedback control solution despite model uncertainties. The proposed slidingmode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine and the generator as well as to electric grid disturbances. The proposed sliding mode control approach has been simulated on a 1.5-MW three-blade wind turbine to evaluate its consistency and performance. The next step was the validation using the National Renewable Energy Laboratory (NREL) wind turbine simulator called the fatigue, aerodynamics, structures, and turbulence code (FAST). Both simulation and validation results show that the proposed control strategy is effective in terms of power regulation. Moreover, the sliding mode approach is arranged so as to produce no chattering in the generated torque that could lead to increased mechanical stress because of strong torque variations.

In this paper, an algorithm for time-varying parameter estimation for a large class of nonlinear systems is presented. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require that the classical persistent excitation condition be satisfied by the input signal. Since the induction motor (IM) is widely used in several industrial sectors, the algorithm developed is potentially useful for adjusting the controller parameters of variable speed drives. The method proposed is simple and easily implementable in real-time. The application of this approach to on-line estimation of the rotor resistance of IM shows a rapidly converging estimate in spite of measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for this IM application also revealed that the proposed scheme is insensitive to the stator resistance variations within a wide range. The merits of the proposed algorithm in the case of on-line time-varying rotor resistance estimation are demonstrated via experimental results in various operating conditions of the induction motor. The experimental results obtained demonstrate that the application of the proposed algorithm to update on-line the parameters of an adaptive controller (e.g. IM and synchronous machines adaptive control) can improve the efficiency of the industrial process. The other interesting features of the proposed method include fault detection/estimation and adaptive control of IM and synchronous machines.

Stabilization of the exact discrete-time models of a class of nonlinear sampled-data systems, with an unknown parameter, is addressed. Given a Lyapunov-based continuous-time adaptive controller that ensures some stability properties for the closed-loop system, a sufficient condition for the design of high order discrete-time controllers is given. The stability analysis is carried out considering the truncated Fliess series of the Lyapunov difference equation. Due to the appearance of power terms of the unknown parameter, the problem is reparameterized in a convex-like form and an estimation law for the new unknown parameter is derived with no need of overparametrization or projection techniques. Then, assuming appropriate conditions hold, high order controllers can be designed. The boundedness of the extended state vector is ensured under some conditions, for a sufficiently small sampling period. It is shown how increasing the controller order can improve system performance.

This paper is devoted to state and parameter estimation for a large class of nonlinear systems using a radial basis function neural network predictor in the continuous time domain. The proof of the convergence of the estimates to their true values is achieved using Lyapunov theories and does not require the classical persistent excitation condition to be satisfied by the input signal. Comparisons between the results obtained and those of the method based on the sliding mode observer are also presented in the case of the estimation of the synchronous machine inductance parameters. The performance exhibited by the obtained results demonstrate that the proposed scheme can also work very well if the stator resistance varies due to the stator winding heating. The comparative results show globally that the proposed algorithm gives better performance than the method based on the sliding mode observer in terms of the convergence rate and the state/parameter accuracies.

In this paper, algorithm for time-varying parameter estimation of a large class of nonlinear systems is presented. This method is based on the variable structure theories. The algorithm developed is potentially useful for adjusting the controller parameters of variable speed drive and can be used for both constant and time-varying parameter estimation. The real-time estimation of the rotor resistance of an induction motor based on this approach, show rapidly converging of estimates in spite of the measurement noise, discretization effects, parameter uncertainties (e.g. inaccuracies on motor inductance values) and modeling inaccuracies. The robustness analysis for induction motor application also revealed that the proposed scheme is insensitive to the stator resistance variations. The merits of the proposed algorithm in the case of time-varying parameter are demonstrated via experimental results in various operating conditions of the induction motor.

In this paper, we study the stabilization of non-linear sampled-data feedback control systems. Our controller design is based on a backstepping procedure applied on the higher order approximations of the exact discretized associated systems.

Thesis submitted in accordance with the requirements of the Normandie University for the degree of PhD Specialty (Electrical Engineering); The research work developed in this thesis has been mainly devoted to the observation and sensorless control problems of electrical systems. Three major contributions have been carried out using the high - gain concept and output feedback adaptive nonlinear control for online emergency power supply. In this thesis, we dealt with the synthesis of sampled high - gain observers for nonlinear systems application to permanent synchronous generators and doubly - fed induction generator. We particularly focus on two constraints: sampling effect and tracking unmeasured mechanical and magnetic state variables. The first contribution consists in a high gain observer design that performs a relatively accurate estimation of both mechanical and magnetic state variable using the available measurements on stator currents and voltages of PMSM. We propose a global exponential observer having state predictor for a class of nonlinear globally Lipschitz system. In a second contribution, we proposed a novel non – standard high gain observer design for non-injective feedback relation application to variable speed DFIGs based wind power generation systems. Meanwhile, a reduced system model is analyzed, provided by observability test to check is it possible synthesis state observer for senseless control. In last contribution, an adaptive observer for states and parameters estimation are designed for a class of state - affine systems application to output feedback adaptive nonlinear control of three-phase AC/DC boost power converter for online emergency power supply systems. Basically, the problem focused on the cascade nonlinear adaptive controller that is developed making use Lyapunov theory. The parameter uncertainties are processed by the control laws under backstepping design techniques with a capacity of adaptation.; Le travail de recherche effectué dans cette thèse a été principalement consacré aux problèmes l’état d’observation et de commande des systèmes électriques sans capteurs. Trois contributions principales ont été faites en utilisant le concept de grand gain et retour de sortie commande adaptative non linéaire pour alimentation électrique de secours en ligne. Cette thèse traite la synthèse d’observateur pour les systèmes non linéaires avec des applications sur la machine synchrones à aimant permanent et machine asynchrone a doublé alimentation. En particulier, nous nous intéressons essentiellement aux contraintes effet d’échantillonnage et d’estimation des variables d'état mécaniques et magnétiques non mesurées. La première contribution est un observateur du type grand gain qui permet de réaliser une estimation relativement précise des variables d’état m´mécaniques et magnétiques des moteurs synchrones à partir des mesures disponibles des courants et tensions statoriques. Nous traitons la synthèse d’observateur à grand gain dynamique pour une classe de systèmes non linéaires globalement Lipschitz à sorties échantillonnées. Dans la deuxième contribution, nous avons proposé un roman non standard HGO pour non injective relation de retour de sortie application à vitesse variable en fonction aux aérogénérateurs asynchrones. Pendant ce temps, un modèle de système réduit est analysé, fourni par le test d'observabilité pour vérifier qu'il est possible observateur d'état de synthèse pour le contrôle sensoriels. Dans la dernière contribution, un observateur adaptatif pour les états et l'estimation des paramètres sont désignés pour une classe de l'état - affins des systèmes l'application à la rétroaction de sortie commande adaptative non linéaire de triphasés AC/DC convertisseur de puissance pour alimentation électrique de secours en ligne. Fondamentalement, le problème concentré sur cascade régulatrice adaptative non linéaire qui est développé fait usage Lyapunov théorie. Les paramètres incertitudes sont traités par les lois de contrôle pratiques dans backstepping techniques de conception avec une capacité d'adaptation.

The research work developed in this thesis has been mainly devoted to the observation and sensorless control problems of electrical systems. Three major contributions have been carried out using the high - gain concept and output feedback adaptive nonlinear control for online emergency power supply. In this thesis, we dealt with the synthesis of sampled high - gain observers for nonlinear systems application to permanent synchronous generators and doubly - fed induction generator. We particularly focus on two constraints: sampling effect and tracking unmeasured mechanical and magnetic state variables. The first contribution consists in a high gain observer design that performs a relatively accurate estimation of both mechanical and magnetic state variable using the available measurements on stator currents and voltages of PMSM. We propose a global exponential observer having state predictor for a class of nonlinear globally Lipschitz system. In a second contribution, we proposed a novel non – standard high gain observer design for non-injective feedback relation application to variable speed DFIGs based wind power generation systems. Meanwhile, a reduced system model is analyzed, provided by observability test to check is it possible synthesis state observer for senseless control. In last contribution, an adaptive observer for states and parameters estimation are designed for a class of state - affine systems application to output feedback adaptive nonlinear control of three-phase AC/DC boost power converter for online emergency power supply systems. Basically, the problem focused on the cascade nonlinear adaptive controller that is developed making use Lyapunov theory. The parameter uncertainties are processed by the control laws under backstepping design techniques with a capacity of adaptation., Le travail de recherche effectué dans cette thèse a été principalement consacré aux problèmes l’état d’observation et de commande des systèmes électriques sans capteurs. Trois contributions principales ont été faites en utilisant le concept de grand gain et retour de sortie commande adaptative non linéaire pour alimentation électrique de secours en ligne. Cette thèse traite la synthèse d’observateur pour les systèmes non linéaires avec des applications sur la machine synchrones à aimant permanent et machine asynchrone a doublé alimentation. En particulier, nous nous intéressons essentiellement aux contraintes effet d’échantillonnage et d’estimation des variables d'état mécaniques et magnétiques non mesurées. La première contribution est un observateur du type grand gain qui permet de réaliser une estimation relativement précise des variables d’état m´mécaniques et magnétiques des moteurs synchrones à partir des mesures disponibles des courants et tensions statoriques. Nous traitons la synthèse d’observateur à grand gain dynamique pour une classe de systèmes non linéaires globalement Lipschitz à sorties échantillonnées. Dans la deuxième contribution, nous avons proposé un roman non standard HGO pour non injective relation de retour de sortie application à vitesse variable en fonction aux aérogénérateurs asynchrones. Pendant ce temps, un modèle de système réduit est analysé, fourni par le test d'observabilité pour vérifier qu'il est possible observateur d'état de synthèse pour le contrôle sensoriels. Dans la dernière contribution, un observateur adaptatif pour les états et l'estimation des paramètres sont désignés pour une classe de l'état - affins des systèmes l'application à la rétroaction de sortie commande adaptative non linéaire de triphasés AC/DC convertisseur de puissance pour alimentation électrique de secours en ligne. Fondamentalement, le problème concentré sur cascade régulatrice adaptative non linéaire qui est développé fait usage Lyapunov théorie. Les paramètres incertitudes sont traités par les lois de contrôle pratiques dans backstepping techniques de conception avec une capacité d'adaptation.