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A simple adaptive feedback strategy is proposed for asymptotic stabilization of nonlinear systems. The approach employs standard feedback control, but employs a novel parameter update strategy based on Newton’s algorithm and a suitable numerical approximation of the system dynamics. Examples and simulation results are included.

International audience; This paper presents the design of an observer for a class of nonlinear Lipschitz sampled-data systems. The proposed observer uses a predictor of the output between the sampling times. This predictor is re-initialized at each sampling time. Besides, unlike the conventional high gain observer, the observer introduced herein has a dynamic observation gain. Using a Lyapunov approach, we will derive an explicit relation between the bound on maximum allowable sampling period and the parameters of the observer in order to guarantee an asymptotic convergence of the observation error.

International audience; This note is dedicated to the stability analysis of bilinear sampled-data systems, controlled via a linear state feedback static controller. A zero order hold device is used. Our purpose is to find a constructive way to calculate the maximum allowable sampling period (MASP) that guarantees the local stability of the system. The proposed stability conditions are formulated as linear matrix inequalities (LMI).

This paper presents two adaptive variable structure identifiers that provide finite time convergent estimate of the induction motor (IM) rotor resistance under feasible persistent of excitation condition. Experiments show that both approaches achieved very good estimation of the rotor resistance which is subjected to online large variation (up to 75%) during operation of the induction motor and are robust with respect to the variation of the stator resistance (up to 100%), measurement noise, modeling errors, discretization effects and parameter uncertainties. The results illustrate globally the superiority of the second approach. Important advantages of the proposed algorithms include that they are suitable for IM adaptive control and are relatively simple to implement in real-time.

International audience; This work presents a set of cascade high gain predictors to reconstruct the vector state of triangular nonlinear systems with delayed output. By using a Lyapunov-Krasvoskii approach, simple sufficient conditions ensuring the exponential convergence of the observation error towards zero are given. All predictors used in the cascade have the same structure. This feature will greatly improve the easiness of their implementation. This result is illustrated by some simulations.

A methodology to design linear proportional-integral (PI) controllers used in power converter applications and ensuring asymptotic stability was proposed. The technique relied on the basic fact that if an affine system can be rendered passive with a constant control, then it is stabilisable with a PI. A structural condition was imposed then on the power converter to satisfy the former property with a passive output generated as a linear combination of the states. This condition is technical and has no clear physical interpretation. This result is extended in three directions: first, the aforementioned condition is removed; second, a larger class of converters (with switching external sources) is considered; third, the load resistance is assumed unknown, and an adaptive PI controller (with three different estimators) is proposed. The methodology is applied to the problem of power factor compensation of a three-phase voltage source rectifier, with simulation results presented and discussed. Also, a stable adaptive PI is designed for the output voltage regulation of a quadratic boost converter showing the performance by means of experimental result.

International audience; This note introduces two notions of desynchronization for interconnected phase oscillators by requiring that phases drift away from one another either at all times or in average. It provides a characterization of each of these two notions based on the grounded variable associated to the system, and relates them to a classical notion of instability valid in Euclidean spaces. An illustration is provided through the Kuramoto system, which is shown to be desynchronizable by proportional mean-field feedback.

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.

We consider the problem of tracking for systems described by input-output equations. Given a desired output, the problem of finding a corresponding tracking control often reduces to solving a singular system of differential equations. Assuming that there exists a formal tracking control, we study regularity of this control at a singular point. Under quite general assumptions we show that existence of formal tracking control implies existence of smooth tracking control. For analytic systems, we show how to determine whether any tracking control in the form of a formal power series (formal tracking control) must necessarily be convergent, or it only has to satisfy certain restrictions on the growth of the coefficients of the formal power series.

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.

In a previous paper the authors proved that a large class of switched power converters can be globally asymptotically stabilized with a simple linear proportional-integral (PI) controller around a suitably defined output. An explicit formula to construct the desired output, which is a linear combination of the converter states, is also given. Moreover, if the load resistance is unknown, an adaptation mechanism is added to the PI preserving the stability properties. In this paper this procedure is applied to a quadratic boost converter yielding a very simple control that is easy to tune. Experimental results assessing the performance of the proposed adaptive PI are shown.

International audience; In this paper, a simple improved direct feedback linearization design method for transient stability and voltage regulation of power systems is discussed. Starting with the classical direct feedback linearization technique currently applied to power systems, an adaptive nonlinear excitation control of synchronous generators is proposed, which is new and effective for engineering. The power angle and mechanical power input are not assumed to be available. The proposed method is based on a standard third-order model of a synchronous generator which requires only information about the physical available measurements of angular speed, active electric power and generator terminal voltage. Experimental results of a practical power system show that fast response, robustness, damping, steady-state and transient stability as well as voltage regulation are all achieved satisfactorily.

International audience; This brief presents an adaptive variable structure identifier that provides finite time convergent estimate of the induction motor rotor resistance under feasible persistent of excitation condition. The proposed rotor resistance scheme is based on the standard dynamic model of induction motor expressed in a fixed reference frame attached to the stator. The available variables are the rotor speed, the stator currents and voltages. Experiments show that the proposed method achieved very good estimation of the rotor resistance which is subjected to online large variation during operation of the induction motor. Also, the proposed online simplified rotor resistance estimator is robust with respect to the variation of the stator resistance, measurement noise, modeling errors, discretization effects and parameter uncertainties. Important advantages of the proposed algorithm include that it is an online method (the value of Rr can be continuously updated) and it is very simple to implement in real-time (this feature distinguishes the proposed identifier from the known ones).

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.

The approach adopted in this paper for the problem of transient stability of multi-machine power systems sees the entire network as the (structure-preserving) interconnection of the network components, described by well known models. These structure-preserving models preserve the identity of the network components and allow for a more realistic treatment of the loads. Our main contribution is the explicit computation of a control law which provides transient stabilization, i.e , prevents the system from loss of synchronism after a large perturbation. The theory is illustrated by means of simulation on a 3-machine power system.

In this paper, we consider the widely popular structure-preserving models used to describe the dynamic behavior of multimachine power systems. These models consist of differential-algebraic equations, where the algebraic constraints stem from the power flow balance between generators, loads, and lines. Our main contribution is the explicit computation of a solution for these algebraic equations under the assumption that the loads are constant impedances. This result is of interest for transient stabilization problems where, to estimate the domain of attraction of a given control law, it is sometimes necessary to explicitly solve the algebraic constraints. Besides this application, we believe this result may be of interest in other problems of power systems analysis.

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.

This paper presents the application of Immersion and Invariance (I&I) strategy to transient stability and voltage regulation of power systems with unknown mechanical power. The proposed controller is made adaptive with respect to the mechanical power by using a finite time estimator. Computing results show the effectiveness of the proposed adaptive nonlinear control strategy.

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.

Tout systeme electronique non lineaire est decrit par un systeme d’equations differentielles non lineaires. Ces dernieres sont resolues de maniere rigoureuse a l’aide du calcul formel et de la theorie des series generatrices a variables non commutatives. Les resultats sont obtenus ici sous forme analytique. Cette particularite permet de representer tout systeme electronique faiblement non lineaire par un modele mathematique caracterise par la serie generatrice associee au signal de sortie de ce systeme. Connaissant le modele de deux circuits non lineaires, nous pouvons obtenir le modele d’un troisieme systeme forme par l’association de ces deux circuits. La serie generatrice de ce nouveau circuit est egale a la composee des series generatrices des circuits initiaux. Ceci facilite la simulation des systemes non lineaires complexes formes de plusieurs circuits plus simples.

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.

In a recent paper we introduced a framework for analysis and design of (possibly nonlinear) power factor (PF) compensators for electrical systems operating in non-sinusoidal (but periodic) regimes with nonlinear loads. In particular, under the standard assumption that the generator is a voltage source with no impedance, we characterized all nonlinear loads whose PF is improved with a given nonlinear compensator. In this brief note we use this framework to study the problem of passive PF compensation of a classical half-bridge controlled rectifier (terminated by a resistor) with non-sinusoidal generator voltage. We give necessary and sufficient conditions for improvement of PF with linear inductors or capacitors, which determine ranges for the compensator parameters that depend on the rectifier fire angle and the amplitude of the voltage source. Given the ?phase advance? operation of the rectifier it is expected that capacitive compensation improves PF, it is however less obvious that this can also be achieved (under some suitable conditions) with inductors.

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.

In this paper, we investigate a new identification algorithm grounded on the Variable Structure theory in order to study parameter and state identification for uncertain nonlinear systems in continuous time. On the one hand, we use a specific observer to estimate unknown states of the studied nonlinear system, based on the Variable Structure theory. On the other hand, the well-known chattering property and the invariance properties, characteristic of the Variable Structure theory, make possible the design of the parameter sliding identifier without the use of the additional persisting excitation condition usually required for the input in order to guarantee the parameter convergence. Stability of the state observer, parameter identifiability of the plant and asymptotic convergence of estimated parameters to their nominal values are studied. Finally, we propose some simulations on a pendulum in order to point out the validity of the methodology even if the input u is not persistently exciting. These numerical simulations highlight the robustness of the state and parameter identification with respect to significant parameter uncertainties thanks to the use of the Variable Structure theory.

In this paper, continuing the line of our previous works, a nonlinear adaptive excitation control is designed for a synchronous generator modeled by a standard third order model on the basis of the physically available measurements of relative angular speed, active electric power and terminal voltage. The power angle, which is a crucial variable for the excitation control, is not assumed to be available for feedback, as mechanical power is also considered as an unknown variable. The feedback control is supposed to achieve transient stabilization and voltage regulation when faults occur to the turbines so that the mechanical power may permanently take any (unknown) value within its physical bounds. Transient stabilization and voltage regulation are achieved by a nonlinear adaptive controller, which generates both on-line converging estimates of the mechanical power and a trajectory to be followed by the power angle that converges to the new equilibrium point compatible with the required terminal voltage. The main contributions here, compared with our previous works, is the use of on-line computation and tracking of equilibrium power angle, and the proof of exponential stability of the closed loop system for states and parameter estimates, instead of the previous asymptotical one.

In this paper, we achieve the semi-global practical stabilization of some nonlinear sampled-data feedback control systems with unknown parameters. Our controller design is based on the higher order approximations of the exact discretized associated systems together with an over parametrization used for the adaptive law, which is due to the appearance of powers series of the unknown parameters in the analytic development of the exact discretized systems.

The transient stabilization problem of a synchronous generator in a multimachine power system is addressed. A robust adaptive nonlinear feedback control algorithm is designed on the basis of a third order model of the synchronous machine in a multimachine power network which takes into account the effect of transfer conductances and of the remote network dynamics: it does not require the machine internal voltage measurement and relies on the knowledge of only three model parameters and of upper and lower bounds on the uncertain ones. Sufficient conditions to be satisfied by the remaining part of the network for guaranteeing L2 and L∞ disturbance attenuation and relative speed regulation are derived which are more general than those required by the single machine-infinite bus approximation.

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.

We establish summation-type conditions to ensure the uniform convergence of discrete-time systems, parameterized in the sampling time. The main results are analogous to previous results obtained in the domain of continuous-time systems and that we have referred to as "integral conditions". The sufficient conditions that we present here can also be interpreted as conditions for convergence of series. Our main results are also useful for design of sampled-data controllers via approximate models; for illustration, we present two results on control design reminiscent of common problems arising in identification and adaptive control.

A novel scheme is proposed for direct speed and flux control of induction motors using on-line estimation of the rotor resistance. The stator current/voltage and the rotor mechanical angular speed are assumed to be measured. The design is based on the variable structure theories. The overall controller combined with on-line rotor resistance adaptation is implemented by using rotor flux and electromagnetic torque observers and the exponential convergence of these observers is demonstrated. The originality of the work is that it considers the unknown rotor resistance as time-varying. The convergence of the estimated rotor resistance is achieved using a Lyapunov-like technique and this guarantees the stability of the rotor speed and flux tracking. Simulation results show the quick convergence of both estimated rotor resistance and tracking of the rotor speed and flux. The robustness analysis revealed that the direct speed and flux tracking schemes are insensitive to the stator/rotor resistance variations (up to 50% for R s and 100% for R r ) and to stator currents measurements noises. The proposed method can also be applied to failure and fault detection since it is easily implementable in real-time.

A new design approach of a sliding mode controller for a generalized electromechanical system with mismatched time-varying uncertainties is presented in this paper. A discontinuous sliding surface can be designed to satisfy a new matching condition, and two layers of sliding surfaces are considered. By maintaining the sliding mode on the first surface, the second layer can be reached in finite time. It is shown that the system is invariant on the sliding surface, and the stability of the closed-loop system is guaranteed. A magnetic levitation example is used to prove the effectiveness of the proposed control scheme.

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.

If an analytic nonlinear plant is (1) dynamically input-output decouplable in neighborhood of the origin and (2) minimum-phase, it is possible to achieve asymptotic tracking for an open set of output trajectories containing the origin in C/sub m//sup n/(0, infinity ), the space of N-times continuously differentiable functions taking values in R/sup m/. It is established in the present work that, for locally controllable MIMO (multiple-input multiple-output) systems, under certain technical assumptions related to system inverses and zero dynamics, conditions (1) and (2) are necessary for the existence of an analytic compensator which yields asymptotic tracking for an open set of output trajectories. Necessary conditions for input-output decouplability in a neighborhood of a given point are also discussed. >

A result of P.E. Crouch and F. Lamnabhi-Lagarrigue (1988) which characterizes Hamiltonian systems described by second-order single-input single-output (SISO) input-output differential equations is extended. A general formalism that defines the adjoint variational equations from the input-output differential equation representation is given. This is used to characterize Hamiltonian systems by using the general results of P.E. Crouch and A.J. van der Schaft (1987), i.e., Hamiltonian systems are those systems for which the variational and adjoint variational systems coincide. >

In this paper we present a robust nonlinear excitation controller of synchronous generator connected through one transmission line to an infinite bus (SMIB) to enhance transient stability. Simulation results show that transient stability of a single machine infinite bus power system under a large sudden fault can be improved.

This paper is dedicated to the identification of parameters for a reasonably large class of nonlinear systems. We propose to design a direct parameter identification method in open loop and an indirect one in closed loop using the variable structure theory. The exponential convergence of parameter identifiers in both cases is studied. Finally, we highlight some robustness properties of the method with respect to bounded parameter perturbations.

A technique for parameters and rotor speed of induction motor using the combination of high-gain observer and radial basis function neuronal predictor is treated in this paper. The algorithms developed here are potentially useful for the design of the drives that can adjust controller parameters automatically. Another possible application is for the detection of failure. In the first scheme, leakage coefficient and rotor time-constant are estimated using the measured rotor speed, stator current and voltage. In the second scheme, rotor resistance is estimated when the rotor speed is available while in the third scheme, both rotor resistance and rotor speed are identified. All the parameters are considered to be time-varying and short-circuit failure is simulated in the rotor resistance. Simulation results illustrate the effectiveness of this technique.

This paper deals with the identification of the stator and rotor resistances. The originality of this work is that it considers both rotor and stator resistances as time-varying parameters. Two schemes for the rotor resistance identification and one scheme for both stator and rotor resistances identification are outlined. The first and the third schemes use the measured stator current, voltage command and rotor speed while the second scheme uses only stator current and voltage commands. All schemes are based on the sliding mode observer and the convergence of the estimates to their true values does not required the persistence of excitation conditions to be satisfied by the input signal.

High order necessary conditions are given for the optimality of totally and partially singular arcs in the case of nonlinear systems not necessarily linear in the control variables. The term 'singular' is used in optimal control problems in which the Pontryagin maximum principle does not furnish in explicit relationship between the control and the state and costate variables. Singular arcs rise in practical applications in rocket and air vehicle flight, and many techniques have been used. In the present study these results are unified and third and higher order necessary conditions are obtained. The necessary conditions are all expressed in terms of the derivative of a particular function along suitable Lie brackets involving vector fields associated with the control process. It is shown that there is a rich interplay between the Lie bracket structure of a system and the qualitative properties of external controls. The result is a key tool in that it provides some suitable control variations in the proof of the theorems. The remaining results are proved by combining Volterra series expansions, special control variations, and multiple integral identities. >

International audience; The problem of observer design is addressed for output-injection nonlinear systems. A major difficulty with this class of systems is that the state equation involves an output-dependent term that is explicitly dependent on unknown parameters. As the output is only accessible to measurement at sampling times, the output-dependent term turns out to be (almost all time) subject to a double uncertainty, making previous adaptive observers inappropriate. Presently, a new hybrid adaptive observer is designed and shown to be exponentially convergent under ad-hoc conditions.

The paper deals with the adaptive limit cycle stabilization problem for an autonomous control system, such that the closed loop system has an orbitally and asymptotically stable periodic solution. Two schemes are proposed: 1) a direct extension of a backstepping procedure, and 2) a scheme which introduces a change of coordinates before using a new Lyapunov function candidate.

The authors develop an algorithm which, given an input-output differential equation, produces a parameterization of the observation space of the underlying state space system. They extend previous results to a more general setting, so that the state space systems, in which the controls enter nonlinearly, can be accommodated, and, more importantly, deal with implicitly defined systems at both the input-output and state space level. Work by M. Flies and H. Hasler (1988) has demonstrated the importance of implicit input-output models, which yield only local state space descriptions. The results here are also important in understanding the realization problem. >

In order to achieve robust output tracking for a class of uncertain triangular systems, a combination of a robust sliding observer and a backstepping procedure is introduced.

Control of a reduced size model of US navy crane using only motor position sensors.- Algorithms for identification of continuous time nonlinear systems: a passivity approach. Part I: Identification in open-loop operation Part II: Identification in llosed-loop operation.- Flatness-based boundary control of a nonlinear parabolic equation modelling a tubular reactor.- Dynamic feedback transformations of controllable linear time-varying systems.- Asymptotic controllability implies continuous-discrete time feedback stabilizability.- Stabilisation of nonlinear systems by discontinuous dynamic state feedback.- On the stabilization of a class of uncertain systems by bounded control.- Adaptive nonlinear excitation control of synchronous generators with unknown mechanical power.- Nonlinear observers of time derivatives from noisy measurements of periodic signals.- Hamiltonian representation of distributed parameter systems with boundary energy flow.- Differentiable lyapunov function and center manifold theory.- Controlling self-similar traffic and shaping techniques.- Diffusive representation for pseudo-differentially damped nonlinear systems.- Euler's discretization and dynamic equivalence of nonlinear control systems.- Singular systems in dimension 3: Cuspidal case and tangent elliptic flat case.- Flatness of nonlinear control systems and exterior differential systems.- Motion planning for heavy chain systems.- Control of an industrial polymerization reactor using flatness.- Controllability of nonlinear multidimensional control systems.- Stabilization of a series DC motor by dynamic output feedback.- Stabilization of nonlinear systems via forwarding mod{L g V}.- A robust globally asymptotically stabilizing feedback: The example of the artstein's circles.- Robust stabilization for the nonlinear benchmark problem (TORA) using neural nets and evolution strategies.- On convexity in stabilization of nonlinear systems.- Extended goursat normal form: a geometric characterization.- Trajectory tracking for ?-flat nonlinear delay systems with a motor example.- Neuro-genetic robust regulation design for nonlinear parameter dependent systems.- Stability criteria for time-periodic systems via high-order averaging techniques.- Control of nonlinear descriptor systems, a computer algebra based approach.- Vibrational control of singularly perturbed systems.- Recent advances in output regulation of nonlinear systems.- Sliding mode control of the prismatic-prismatic-revolute mobile robot with a flexible joint.- The ISS philosophy as a unifying framework for stability-like behavior.- Control design of a crane for offshore lifting operations.- New theories of set-valued differentials and new versions of the maximum principle of optimal control theory.- Transforming a single-input nonlinear system to a strict feedforward form via feedback.- Extended active-passive decomposition of chaotic systems with application to the modelling and control of synchronous motors.- On canonical decomposition of nonlinear dynamic systems.- New developments in dynamical adaptive backstepping control.