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The Lyapunov exponent method is generally used for classifying hyperchaotic, chaotic, and regular dynamics based on the equations modeling the system. However, several systems do not benefit from appropriate modeling underlying their dynamic behaviors. Therefore, having methods for classifying hyperchaotic, chaotic, and regular dynamics using only the observational data generated either by the theoretical or the experimental systems is crucial. In this paper, we use single nonlinear node delay-based reservoir computers to separate hyperchaotic, chaotic, and regular dynamics. We show that their classification capabilities are robust with an accuracy of up to 99.61% and 99.03% using the Mackey–Glass and the optoelectronic oscillator delay-based reservoir computers, respectively. Moreover, we demonstrate that the reservoir computers trained with the two-dimensional Hénon-logistic map can classify the dynamical state of another system (for instance, the two-dimensional sine-logistic modulation map). Our solution extends the state-of-the-art machine learning and deep learning approaches for chaos detection by introducing the detection of hyperchaotic signals.

In this paper, an adaptive nonlinear method is proposed for both Maximum Power Point Traking Control and output voltage regulation of a Single-Ended Primary Inductance Converter (SEPIC). The main objective of this adaptive synergetic controller is to maintain a constant switching frequency of the converter and to stabilize the output voltage under uncertain weather conditions in real time. A hybrid control scheme has been derived and a Radial Basis function neural network used for approximation of unmeasurable or unmeasured variables. The effect of the controller to provide a maximum power transfer from PV side to SEPIC converter under unpredictable weather conditions and load variations has been verified through simulations. The stability of the close-loop system is insured by Lyapunov’s theory and the proposed algorithm gives good results compared to the Sliding Mode Controller used in the same context.

This paper presents an intelligent control technique using a nonlinear neuro-adaptive method. This method is based on a nonlinear model describing the dynamics of the boost converter, the PV array and the load for maximum power point tracking (MPPT) under varying environmental conditions. The proposed approach consisted of a radial basis function-neuro observer for online estimation of unknown PV system parameters (i.e., irradiation and temperature) and an online trained neuronal controller that ensures a satisfactory MPPT, whatever be the position of the photovoltaic panel. The real-time implementation of the proposed controller is achieved using Arduino Mega board. The performance of the proposed MPPT method is analyzed under different operating conditions and compared to those provided by the P&O method. Simulation results using MATLAB/Simulink software coupled to experimental results demonstrate the feasibility and the robustness of the proposed controller.

Control of coexisting attractors and chaos in brushless direct current motor (BLDCM) is investigated in this paper. Using a weak harmonic modulation of a parameter of the BLDCM, the coexisting attractors between chaotic and period-1-oscillations are destroyed and transformed to period-1-oscillations. Moreover, two single controllers are designed to control the chaotic behavior found in BLDCM. Numerical simulations are used to evaluate the performance of the two developed controllers. Finally, the dynamical behaviors found in BLDCM and the physical feasibility of two proposed single controllers are validated through circuit implementations on OrCAD-PSpice software. The robustness of the three controllers is tested by a sensitivity analysis to parametric uncertainties.

This work proposes an alternative strategy to the use of a speed sensor in the implementation of active and reactive power based model reference adaptive system (PQ-MRAS) estimator in order to calculate the rotor and stator resistances of an induction motor (IM) and the use of these parameters for the detection of inter-turn short circuits (ITSC) faults in the stator of this motor. The rotor and stator resistance estimation part of the IM is performed by the PQ-MRAS method in which the rotor angular velocity is reconstructed from the interconnected high gain observer (IHGO). The ITSC fault detection part is done by the derivation of stator resistance estimated by the PQ-MRAS estimator. In addition to the speed sensorless detection of ITSC faults of the IM, an approach to determine the number of shorted turns based on the difference between the phase current of the healthy and faulty machine is proposed. Simulation results obtained from the MATLAB/Simulink platform have shown that the PQ-MRAS estimator using an interconnected high-gain observer gives very similar results to those using the speed sensor. The estimation errors in the cases of speed variation and load torque are almost identical. Variations in stator and rotor resistances influence the performance of the observer and lead to poor estimation of the rotor resistance. The results of ITSC fault detection using IHGO are very similar to the results in the literature using the same diagnostic approach with a speed sensor.

In this paper we investigate a new modulation technique for the control signals of grid-connected PV inverters. The inverters are connected to the grid via an L-filter. This technique improves the quality of the energy injected into grid by minimising the THD of the currents. Indeed, the harmonics of currents higher than the fundamental and DC component are the cause of several disturbances and electrical damage. Recent studies have shown that the THD of the currents injected into the grid by PV inverters become more important for low sunshine conditions. We propose a solution to this problem using a signal modulation technique that takes into account the advantages of PWM and DCM techniques. An adaptive law of the control gain is proposed in order to establish a new control law that helps to minimize the THD of the current. The results presented validate the approach discussed and rank our method above conventional PWM. The performances of this new method give it a special attention in power quality management.

This paper presents a new direct fuzzy control scheme for a decentralized wind power generation system with a wide operation range of wind speed variations. The proposed scheme makes use of direct fuzzy torque and power control laws to improve the performance of the system and more particularly the quality of energy inserted into the electrical grid. The direct fuzzy torque control law is used to generate the control signal of the converter on the generator side in order to extract the maximum power available at the turbine and also to maintain the stator flux of the machine at an acceptable level while the direct fuzzy power control law of the converter is used on the grid side to obtain a power efficiency close to unity. A comparison of simulation results between the proposed control method and the classical direct control method found in the literature, shows that the proposed control method provides better performance by improving trajectory tracking as well as robustness against variations in the internal parameters of the asynchronous generator (rotor resistance and stator resistance). It also ensures a supply of quality electrical energy into the electrical grid.

In this contribution, a new elegant hyperjerk system with three equilibria and hyperbolic sine nonlinearity is investigated. In contrast to other models of hyperjerk systems where either hidden or self-excited attractors are obtained, the case reported in this work represents a unique one which displays the coexistence of self-excited chaotic attractors and stable fixed points. The dynamic properties of the new system are explored in terms of equilibrium point analyses, symmetry and dissipation, and existence of attractors as well. Common analysis tools (i.e., bifurcation diagram, Lyapunov exponents, and phase portraits) are used to highlight some important phenomena such as period-doubling bifurcation, chaos, periodic windows, and symmetric restoring crises. More interestingly, the system under consideration shows the coexistence of several types of stable states, including the coexistence of two, three, four, six, eight, and ten coexisting attractors. In addition, the system is shown to display antimonotonicity and offset boosting. Laboratory experimental measurements show a very good coherence with the theoretical predictions.

The dynamics of indirect field oriented control (IFOC) of 3-phase induction motor is studied in this paper. The dynamical behaviors of the studied system are performed using bifurcation diagrams, maximum Lyapunov exponent plots, phase portraits, and isospike diagram. The numerical simulation results reveal that the IFOC of 3-phase induction motor displays coexistence of attractors for the same set of IFOC of 3-phase induction motor parameters, periodic and chaotic bursting oscillations. Basins of attraction of different competing attractors are plotted showing complex basin boundaries. The numerical simulation finding are validated by the OrCAD-Spice results.

This paper presents a new synergetic control scheme for grid-connected photovoltaic (PV) systems. The proposed control scheme improves the quality of energy injected by the PV inverter into the grid by minimizing the total harmonic distortion (THD) of currents during low sunlight conditions. The proposed technique makes use of an intermediate virtual control to design the new synergetic control law which is applied on a grid-connected PV system supplying linear and non-linear loads. The effectiveness of the control scheme is simulated in Matlab/Simulink software. A comparison of simulation results with other linear and nonlinear control techniques in the literature show the effectiveness, superiority and satisfactory performance of the proposed scheme in minimizing the harmonics and THD of the currents under low solar irradiance. The results also show that the proposed synergetic controller is robust and has better dynamic performance under varying irradiance conditions.

Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic applications generally focus on the spectral content of EEG, which is the type of neural oscillations that can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and motivates the need for advanced signal processing techniques to aid clinicians in their interpretations. This paper describes the application of Wavelet Transform (WT) for the processing of Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for feature selection and dimensionality reduction where the informative and discriminative two-dimension features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP) neural network. For five classification problems, the proposed model achieves a high sensitivity, specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed methods and those obtained with previous literature methods shows the superiority of our approach for EEG signals classification and automated diagnosis.

This paper focuses on the dynamical analysis of the permanent magnet asynchronous motor with the aim of subsequently designing effective robust control laws for the indirect field-oriented control (IFOC) devices. We first perform some tasks which demonstrate the existence of chaos phenomenon in the IFOC using relevant indicators such as phase portraits, bifurcations diagrams and Lyapunov exponents. Chaotic signature and some striking transitions are revealed such as period-doubling, torus, period-adding and chaos when an accessible parameter of the IFOC motor is changed. More interestingly, a certain range of the parameter space corresponds to the transient chaos. This behavior was not reported previously and can be considered as an enriching contribution. Secondly, due to the great interest to reduce the upper bound of uncertainties and interference, conventional sliding mode control (SMC) has been abundantly investigated for fault-tolerant control (FTC) systems. However, this approach presents several drawbacks in terms of overshoot, less robustness, transient state error, large chattering and speed of convergence that limit its use for industrial applications. For these reasons, the integral sliding mode control (ISMC) and the fuzzy sliding mode control (FISMC) are proposed to keep the IFOC motor in the regular operation zone. The optimal feedback gains and a sufficient condition are proposed for the stability of the overall IFOC system is drawn based on the linear quadratic regulator (LQR) method. To highlight the effectiveness and applicability of the proposed control scheme, numerical simulation results are presented. This analysis allows us a great knowledge of engineers for interpreting the operation of the IFOC motor. To highlight the effectiveness and the applicability of the proposed control scheme, numerical simulations results are presented and clearly demonstrated the feasibility of these techniques.

This paper presents a new hybrid approach which combines Radial Basis Function neural network with the sliding mode technique to design a Static Synchronous Series Compensator controller for power flow control and DC voltage regulation of an electric power transmission system. The proposed neuro-sliding mode control technique requires reduced number of hidden units and the weights are updated through on-line learning. The proposed technique is robust and does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system (as opposed to existing methods based on Radial Basis Function neural networks). The performance of the proposed controller is validated on an IEEE 9 bus test system. Comparative results with another neural networks control and PI regulators show the effectiveness and satisfactory performance of the proposed controller in dealing with the perturbations considered.

One of the main challenges in distributed generation is to keep supplying some priority loads when islanding occurs. Unfortunately, most anti-islanding protection (AIP) methods fail in islanding detection if the demand in the islanded loads matches the production in the island. Many active AIP schemes are too slow and cause power quality problems. In this paper, an islanding detection method for inverter-based photovoltaic system (PVS) is presented, operating with a simple adaptive loads shedding algorithm. This method is based on modulating the inverter output voltage at the point of common coupling. This frequency modulation consists in introducing interharmonics as side bands of the inverter voltage. After islanding detection, the adaptive loads shedding algorithm operates so as to keep powered priority loads as possible. AIP scheme and PVSs are implemented in MATLAB/Simulink environment. The effectiveness of the proposed scheme was tested and evaluated under a wide range of operating conditions. These tests determined that the proposed technique does not exhibit any non-detection zone and detects islanding within five electric cycles. No significant effect on power quality is recorded even during islanding detection time. The proposed technique was also robust and more independent of system parameters than some others.

Electricity access in remote areas of Sub-Saharan Africa is limited due to high costs of grid extension to areas characterised by low population and low energy densities. Photovoltaic hybrid systems can be computed using an energy balance equation involving one unknown. For hypothetical village with an average daily energy demand of 153.6 kWh/d, the monthly energy output of photovoltaic modules at Garoua, Cameroon, enabled the evaluation of feasible photovoltaic hybrid (PVHS) options. An option with a renewable energy fraction of 0.557 having lower initial investments is suggested for electrification of more remote villages in Sub-Saharan African countries which have high solar radiation levels. This option comprises a 23.56 kWp PV array, a 15 kWp PV inverter, a 25 kW bi-directional inverter, a battery bank of capacity 324.48 kWh and a 25 kW diesel generator with an operating time of 1309 h/yr or 3.59h/d. The size of the PV array determined is smaller compared to the sizes of PV arrays which have been evaluated in the range 30-45 kWp using HOMER software for medium villages in Senegal. L’access à l’éléctricité en Afrique Sub-saharienne est limité par le cout élevé d’extension des resaux electriques vers les zones isolées caracterisés par une faible densité de la population et une faible densité de consummation de l’energie electrique. Les systemes hyrides photovoltaiques peuvent étre evalués avec une equation energétique à un inconue. Pour un village hypothetique avec une demande énergétique quotidienne de 153.6 kWh/jr, les productions énergetique mensuelles des modules photovotaiques à Garoua, au Cameroun, ont permis l’evaluation des options photovoltatiques hybrides realisables. Une option avec une fraction d’énergie renouvelable de 0,557 ayant un investment initial inféreur est suggerée pour l’électrification de villages plus éloignés dans l’Afrique subsaharienne qui ont des niveaux élevés de rayonnement solaires. Cette option comprend un générateur photovotaique de 23,56 kWc, un onduleur photovoltaique de 15 kWc, un inverseur bidirectionnel de 25 kW, un groupe de batterie d’une capacité de 324.48 kWh, et un groupe electrogene diesel de 25 kW avec un temps de fonctionnement de 1309 h/an ou 3.59h/j. La taille du générateur photovoltaique determiné est inférieur à celle des générateurs photovoltaiques dans la plage allant de 30 à 45 kWc à l’aide du logiciel HOMER pour les villages moyenne du Senegal comptant 750 habitants.

International audience; In this technical note, an adaptive control framework for compensation of uncertainties and perturbations that satisfy the matching condition on a class of nonlinear dynamic systems is presented. The proposed method does not need the explicit knowledge of the bound values on the uncertainties, and the resulting compensatory term is continuous. The application of this formulation for maximum power point tracking and unity power factor on grid-connected photovoltaic systems is presented.

In this paper, a new control strategy of a variable speed based SEIG wind energy conversion is presented. Nonlinear control strategy is proposed to extract maximum available energy from the wind turbine and regulate the rotor flux magnitude of SEIG and the DC-link voltage in the generator side as well as the load voltage magnitude in the inverter load side under wind speed, load and rotor/stator resistances variations. The proposed nonlinear adaptive controller is robust given its insensitivity to SEIG variation of the rotor and stator resistances. The global convergence and stability analysis taking into account the interconnections between the stator electrical angular position, the rotor flux and rotor resistance estimators and the nonlinear controller have been proved by using the separation principle. Another contribution of this paper is the robustness of the proposed method with respect to the variation of the driven speed and relatively low wind speed operation. Thus, the proposed stand-alone variable speed based SEIG wind energy conversion system can be used in remote and isolated areas where the mean value of the wind speed profile is relatively low.

In this work, a novel adaptive control scheme that allows driving a stand-alone variable-speed wind turbine system to its maximum power point is presented. The scheme is based on the regulation of the optimal rotor speed point of the wind turbine. In order to compute the rotor speed reference, a model-based extremum-seeking algorithm is derived. The wind speed signal is necessary to calculate this reference, and a novel artificial neural network is derived to approximate this signal. The neural network does not need off-line learning stage, because a nonlinear dynamics for the weight vector is proposed. A block-backstepping controller is derived to stabilize and to drive the system to the optimal power point; to avoid singularities, the gradient dynamics technique is applied to this controller. Numerical simulations are carried out to show the performance of the controller and the estimator. Copyright © 2016 John Wiley & Sons, Ltd.

This paper presents a simple control structure using rotor flux oriented vector technique for a stand-alone permanent magnet synchronous generator (PMSG) associated to PWM rectifier, PWM DC–DC converter and PWM inverter. The DC–DC converter feeding an electronic load is controlled to maintain the dc-bus voltage at a desired constant value. The isolated PMSG is assumed to be driven by a variable speed wind turbine. A sliding mode control strategy has been developed to regulate the direct axis current, ensure maximum power tracking issue as well dc-bus voltage regulation. The online estimation of the rotor flux using sliding mode concept is provided. The control and estimator stability analysis is based on Lyapunov theory. Simulation results, carried out in Matlab/Simulink software, are presented to demonstrate the effectiveness of the proposed control method when the control system operates under load variations and parameters uncertainties. The proposed adaptive control can be used in remote area where wind speed is low.

The bi-stability property and transition to hyperchaos of a class of semiconductor diode-based oscillators are investigated. A simple 4D hyperchaotic oscillator proposed by Lindberg and co-workers (referred to as the LMT oscillator hereafter) is considered as a paradigm. Due to the usage of stable oscillators coupled through a non-linear resistor, this circuit has better reproducibility and higher stability and thus may be exploited for secure communication applications. In contrast to current approaches based on piecewise-linearization methods, a smooth mathematical model is derived to investigate the dynamics of the oscillator. The bifurcation analysis shows some striking transitions including period-adding, period doubling and torus breakdown routes to chaos when monitoring the control parameters in tiny ranges. More interestingly, some regions of the parameter space corresponding to the coexistence of different attractors are revealed. This phenomenon was not reported previously and thus represents an enriching contribution concerning the behaviour of such types of oscillators. The transitions to hyperchaos are contrasted with equivalent scenarios obtained from an experimental implementation of the circuit in PSpice yielding a very good agreement.

This paper presents a simple and robust control strategy for a variable speed wind turbine conversion system using a squirrel-cage induction generator and a three-phase voltage source (AC/DC/AC) Pulse Width Modulation (PWM) converter connected to the utility grid through an LCL filter. The control strategy integrates for the generator side an adaptive radial basis function (RBF) neurosliding mode controller associated with the rotor flux oriented vector control which is used to regulate the turbine rotation speed, rotor flux, and the DC bus voltage. For the grid side, the inverter current and voltage regulation as well as the current injected into the grid are regulated by PI controllers for two modes of operation, namely, the stand-alone mode and grid connected mode. The main contribution of this article is the introduction of a new and simple control algorithm allowing automatic mode switching method based on wind speed. The proposed scheme is very efficient and can be easily implemented in practice. Simulation results illustrate the effectiveness and feasibility of the proposed algorithm.

In this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural network identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme.

In this paper, a decentralized nonlinear coordinated excitation and steam valve adaptive control combined with a modified high-order sliding mode differentiator is designed for multi-machine power system stability enhancement. The proposed control scheme is based on Lyapunov’s direct method and requires only local information on the physically available measurements of relative angular speed, active electric power and terminal voltage with the assumption that the power angle and mechanical power input are not available for measurement. Each synchronous generator is considered as a classical fifth order model that includes turbine dynamics. The simplicity of the proposed scheme and its robustness with respect to large perturbations, change in operating point and parameter uncertainties constitute the main positive features. Simulation results in the case of the Kundur 4-machines 2-area power system show the effectiveness, robustness and superiority of the proposed scheme over the classical AVR/PSS.

This paper presents a decentralized coordinated excitation and steam valve adaptive control combined with a high-order sliding mode differentiator. The aim is to obtain high performance for the terminal voltage and the rotor speed simultaneously under a sudden fault and a wide range of operating conditions. The methodology adopted is based on second order sliding mode technique using the supper twisting algorithm. The proposed scheme requires only local information on the physically available measurements of relative angular speed, active electric power and terminal voltage with the assumption that the power angle and mechanical power input are not available for measurement. It can be implemented locally and dispersedly for individual generators and is convenient for industrial applications. Simulation results in the case of the Kundur 4-machines 2-area power system show the effectiveness, robustness and superiority of the proposed scheme over the classical AVR/PSS controller and steam valve PI regulator.

We investigate the dynamics of an inductor-free hyperchaotic oscillator introduced by Giannakopoulos and Deliayannis (Int J Electron 92:143–159, 2005). This oscillator is constructed by coupling an LC oscillator to the Deliyannis single amplifier biquad, which has been turned into an oscillator, by means of a semiconductor diode. Based on the Shockley exponential diode equation, we propose a smooth mathematical model for a better description of both the regular and chaotic dynamics of the hyperchaos generator. Various bifurcation diagrams and corresponding graphs of Lyapunov exponents are plotted to summarize different transitions to chaos/hyperchaos. One of the most striking and interesting finding of this work is the existence of two completely different families of solutions/attractors (depending on the choice of circuit parameters’ values) related to the classical period-doubling route to chaos or to the torus breakdown transition to chaos (which finally gives rise to a hyperchaotic attractor) when monitoring a single circuit component (i.e. the negative resistor). The physical implementation of the oscillator is considered to validate the theoretical analysis through several comparisons between experimental and numerical results. The results of present investigations may be exploited for rigorous designs of such types of oscillators in various engineering applications as well as for educational purposes.

An adaptive nonlinear controller for transient stability and voltage regulation of power systems based DFIG in multimachine configuration is presented using a standard third order dynamical model of the DFIG. Finite time estimators for the unmeasurable time derivative of the quadrature component of the DFIG stator current, mechanical input, unknown direct axis transient open circuit time constant (function of the rotor resistance) are presented. The main feature of the proposed control scheme is its robustness with respect to large perturbations and parameter variations. Numerical results are presented to illustrate the performance of the proposed control scheme and its robustness properties.

The dynamics of a simple autonomous jerk circuit previously introduced by Sprott in 2011 are investigated. In this paper, the model is described by a three-time continuous dimensional autonomous system with an exponential nonlinearity. Using standard nonlinear techniques such as time series, bifurcation diagrams, Lyapunov exponent plots, and Poincaré sections, the dynamics of the system are characterized with respect to its parameters. Period-doubling bifurcations, periodic windows, and coexisting bifurcations are reported. As a major result of this work, it is found that the system experiences the unusual phenomenon of asymmetric bistability marked by the presence of two different attractors (e.g., screw-like Shilnikov attractor with a spiralling-like Feigenbaum attractor) for the same parameters setting, depending solely on the choice of initial states. Among few cases of lower dimensional systems capable of such type of behavior reported to date (e.g., Colpitts oscillator, Newton–Leipnik system, and hyperchaotic oscillator with gyrators), the jerk circuit/system considered in this work represents the simplest prototype. Results of theoretical analysis are perfectly reproduced by laboratory experimental measurements.

This article proposes a new decentralized sliding mode control design for two synchronous generators driven by marine turbines. Decentralized Sliding Mode Multimachine Control (DSMMC) is applied to a third-order model of the wound rotor synchronous generator in multimachine configuration. Our goal consists in regulating frequency and voltage of synchronous generators in order to ensure their direct interconnection. In addition, the control strategy named Maximum Power Point Tracking (MPPT) is used to maximize the energy production. Simulations in Matlab environment are realized for a semidiurnal type tide (tide of period 12 h 25 min) using data of Raz de Sein (Britany, France). Simulations results are presented in order to highlight robustness properties under mechanical and electrical perturbations. Finally, our control method is compared to Decentralized NonLinear Control (DNLC) and conventional AVR–PSS.

Biomedical image processing is experiencing a significant progress with many applications. However, automatic recognition of microscopic pathogens from their images remains a challenge that will allow clinical laboratories to increase both the speed of tests and the reliability of diagnoses. We present an algorithm for edge detection of parasites in microscopic images of stools, using the multi-scale wavelet transform. This method is an evolution of the Canny–Mallat detector which gives the possibility to vary the frequency of the analysis in order to find the outlines of the most significant edges. The various contours obtained are chained across the scales from the coarsest to the finest. Using this algorithm, we were able to correctly represent the contours of the features of parasites found in microscopic images. The results obtained were compared with those produced by classical edge detectors on the same images. It comes out from both subjective and objective quantitative performances evaluation that our detector, better than all others, can clearly mark the outlines of the structures of the pathogen on an image of stools.

This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

This article presents the decentralized sliding mode control design for two marine turbines connected to Grid. MPPT strategy control (Maximum Power Point Tracking) is used to maximize the energy production. Simulations in Matlab environment are made over one tide period of semidiurnal type and under some perturbations using data of Raz de Sein (Britany, France). In the first part of the simulation, the obtained results show that the energy production system can be directly connected to grid. In the second part of simulations, a comparative result using the proposed method and conventional AVR-PSS is discussed.

International audience; In this paper, a new hybrid method which combines radial basis function (RBF) neural network with a sliding-mode technique to take advantage of their common features is used to control a class of nonlinear systems. A real-time dynamic nonlinear learning law of the weight vector is synthesized and the closed-loop stability has been demonstrated using Lyapunov theory. The solution presented in this work does not need the knowledge of the perturbation bounds, neither the knowledge of the full state of the nonlinear system. In addition, the bounds of the nonlinear functions are assumed to be unknown and the proposed RBF structure uses reduced number of hidden units. This hybrid control strategy is applied to extract the maximum available energy from a stand-alone self-excited variable low-wind speed energy conversion system and design the dc-voltage and rotor flux controllers as well as the load-side frequency and voltage regulators assuming that the measured outputs are the rotor speed, stator currents, load-side currents and voltages despite large variation of the rotor resistance and uncertainties on the inductances. Finally, simulation results compared with those obtained using the well-known second-order sliding-mode controller are given to show the effectiveness and feasibility of the proposed approach.

An online rotor resistance estimator for induction motor adaptive control is presented. The rotor resistance scheme uses the rotor speed, the stator current, voltage provided by the controller, time-derivatives of the stator current and voltage. The time-derivatives of the stator current and voltage are estimated using 2nd order sliding mode observer. The finite time convergence of the estimated rotor resistance to the nominal value is achieved when the classical persistent excitation condition is satisfied by the input signal. The method is suitable for online operation to continuously update the rotor resistance value. Experimental results with online variation of the rotor resistance show that the proposed algorithm gives better performance compared to the previous work. The other interesting feature of the proposed method is the robustness properties with respect to the variation of the stator resistance both at relatively high speed and low speed.

International audience; A nonlinear adaptive excitation controller is proposed to enhance the transient stability and voltage regulation of synchronous generators with unknown power angle and mechanical power input. 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 relative angular speed, active electric power, infinite bus and generator terminal voltages. The operating conditions are computed online using the above physical available measurements, the terminal voltage reference value and the estimate of the mechanical power input. The proposed design is therefore capable of providing satisfactory voltage in the presence of unknown variations of the power system operating conditions. Using the concept of sliding mode equivalent control techniques, a robust decentralized adaptive controller which insures the exponential convergence of the outputs to the desired ones, is obtained. Real-time experimental results are reported, comparing the performance of the proposed adaptive nonlinear control scheme to one of the conventional AVR/PSS controller. The high simplicity of the overall adaptive control scheme and its robustness with respect to line impedance variation including critical unbalanced operating condition and temporary turbine fault, constitute the main positive features of the proposed approach.

This paper presents the adaptive nonlinear controller for power system stabilization and voltage regulation that is capable of providing satisfactory voltage in the presence of unknown variations of the power system operating conditions. The design is based on a standard third-order model of synchronous generator which requires only information about the physical available measurements of relative angular speed, active and reactive powers and terminal voltage. The power angle, mechanical power and the total reactance, are not assumed to be available for feedback. The proposed adaptive controller is implementable in practice since only measurable outputs have been used. The robustness with respect to 3–phase short-circuit and mechanical power variation constitutes the main positive features of the proposed approach. The results of accurate numerical simulations are reported, comparing the performance of the proposed adaptive nonlinear control scheme to one of the AVR/PSS controller.

In this paper, an online rotor resistance estimator for induction motor adaptive control is presented. The proposed algorithm is an improvement of the previous work Kenne et al.. The rotor resistance scheme uses the rotor speed, the stator current, voltage provided by the controller, time-derivatives of the stator current and voltage. The time derivatives of the stator current and voltage are estimated by using 2 nd order sliding mode observer. The convergence of the estimated rotor resistance to the nominal value in finite time is achieved when the classical persistent excitation condition is satisfied by the input signal. Experimental results with 100% variation of the stator resistance and online variation of the rotor resistance show that the proposed algorithm gives better performance compared to the previous work.

This paper deals with on-line state and parameter estimation of a reasonably large class of nonlinear continuous-time systems using a step-by-step sliding mode observer approach. The method proposed can also be used for adaptation to parameters that vary with time. The other interesting feature of the method is that it is easily implementable in real-time. The efficiency of this technique is demonstrated via the on-line estimation of the electrical parameters and rotor flux of an induction motor. This application is based on the standard model of the induction motor expressed in rotor coordinates with the stator current and voltage as well as the rotor speed assumed to be measurable. Real-time implementation results are then reported and the ability of the algorithm to rapidly estimate the motor parameters is demonstrated. These results show the robustness of this approach with respect to measurement noise, discretization effects, parameter uncertainties and modeling inaccuracies. Comparisons between the results obtained and those of the classical recursive least square algorithm are also presented. The real-time implementation results show that the proposed algorithm gives better performance than the recursive least square method in terms of the convergence rate and the robustness with respect to measurement noise.