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Both input current and output voltage control loop of DC - DC boost converter based on a serial Integral and Proportional (IP) controller is presented. The performances of these controllers are validated through extensive simulation results using Matlab/Simulink software packages.

In automotive application, electric vehicles and electrical busses are based essentially on electrical machine. Controlling the motor electric current is the only possible solution for varying it speed. This kind of application needs a high torque/speed relation factor for moving this transportation system (load). Brushless direct current (BLDC) machine is one of the most motors who can be used for this system, however the BLDC motors performances are highly affected by the disturbance of the load. Controlling its speed needs generally a high precision control method and needs knowing the motor parameters for finding the optimal regulator parameters. Basing on various solutions, it is possible to calculate these parameters without necessity finding the motor parameters. Particle swarm optimization (PSO) method is one of the optimization solutions who can resolve this problem. Therefore, this paper deals with the implantation of this optimization tool for help finding the optimal proportional-integral (PI) parameters for controlling the BLDC speed.

This paper focuses on a maximum power point tracking (MPPT) algorithm based on the first order sliding mode approach. This work is aimed at systems that are fed with a photovoltaic (PV) generator. The main objective is to act on the panel to ensure that the energy collected is always at its maximum. Because a PV panel frequently suffers from non-linearity of its P-V curves, we propose to work with MPPT controllers based on the first order sliding mode approach; indeed, this approach in general is recognized as one of the efficient tools to design robust controllers. It has received much more attention within the last two decades, and many researchers are dealing with this type of robust controller to ensure optimal operating point of a PV system. The proposed MPPT algorithm has been implemented using a DSPACE DSP (digital signal processor) controller. To demonstrate the efficiency and the validity of the developed algorithm in real time, an experimental setup around a boost converter and a resistive load is experimentally studied and successfully implemented. The real-time validation and the experimental results show that the proposed algorithm can effectively improve the efficiency of PV array output under climatic variation and load change. It is a favorable algorithm that is easy to realize.

DC-DC boost converters which provide dc output voltage are widely used in several applications such as photovoltaic power systems, hybrid vehicle, cellphones… Maintaining a constant output voltage of the DC-DC boost converter under variable input voltage and load changes have always attracted the attention of many researchers and many studies have been published on this issue. This paper, present an averaged model of DC-DC boost converter in continuous conduction mode (CCM). A single loop control is generally used to regulate the output voltage of the boost converter. In this paper, a cascade loop control includes two cascaded control loops; internal current control, and external voltage control is used to improve control performance. Subsequently, Proportional-Integral (PI) controller is used to tracks the input desired reference signal and eliminates the load disturbances. The mathematical model and the controller are implemented in the MATLAB/SIMULINK environment. Simulations results are presented and discussed to evaluate the performance of the controller.

the Indirect Field Oriented Control IFOC is applied to the Dual Star Induction Motor DSIM. The indirect field oriented control based on Space vector modulation SVM technique is a common solution used to overcome conventional IFOC problems; such as torque ripple and switching frequency variation. However, this control technique relies on the orientation of the rotor flux and the PI controllers. The actual article based on improving the performance of SVM-IFOC by using the sliding mode controller SMC instead of the PI controllers. This controller can solve the classic PI problems which are represented in robustness with respect to the variation of the parameters and the external disturbances and gives excellent dynamic performances in a game of speeds. Furthermore, the mode controller is based on a strategy with a sat’ function to reduce the chattering effect. The effectiveness of the wholecompound control method (SVM-IFOC by sliding mode controller) is studied in different robustness tests with simulation using Matlab/Simulink.

In this paper, a mathematical model of the photovoltaic (PV) pumping system s main components is firstly established. Then, the design of maximum power point tracking (MPPT) stage that ensures battery charging is described. This work is motivated by the need of photovoltaic generator (PVG) that efficiently extracts maximum power. The PVG is a special source of energy which has nonlinear current-voltage characteristics depending on variations in temperature and solar irradiance. In order to achieve the MPPT operating goals, a special interest is focused on the variable structure sliding mode (SM) control strategy and the classic perturb and observe (P&O) algorithm. The permanent magnet synchronous motor (PMSM) is selected as a pump driver. The field oriented control is performed as the motor drive strategy. Simulation results show a high level of efficiency, obtained with the proposed PV based pumping system. The performance comparison between SM controller and P&O controller has been carried out to demonstrate the effectiveness of the former in drawing more energy and a fast response against irradiation disturbances.

In this work, an intelligent artificial control of a variable speed wind turbine (PMSG) is proposed. First, a mathematical model of turbine written at variable speed is established to investigate simulations results. In order to optimize energy production from wind, a pitch angle and DC bus control law is synthesized using PI controllers. Then, an intelligent artificial control such as fuzzy logic and artificial neural network control is applied. Its simulated performances are then compared to those of a classical PI controller. Results obtained in MATLAB/Simulink environment show that the fuzzy and the neuro control is more robust and has superior dynamic performance and hence is found to be a suitable replacement of the conventional PI controller for the high performance drive applications.

This paper presents an improved speed estimator for a permanent magnet synchronous motor (PMSM). It focuses on hybrid electric vehicles (HEVs). The speed estimator is based on reactive power model reference adaptive system (Q-MRAS). The MRAS parameters are tuned using particle swarm optimization (PSO) algorithms. The proposed method has been experimentally verified with a 100 kW, 5000 rpm PMSM, and a good agreement between the measured speed and the estimated speed is found. It is shown that the proposed method is able to handle the transition into the flux weakening mode without any problem.

This paper discusses a comparative study of two modeling methods based on multimodel approach. The first is based on C-means clustering algorithm and the second is based on K-means clustering algorithm. The two methods are experimentally applied to an induction motor. The multimodel modeling consists in representing the IM through a finite number of local models. This number of models has to be initially fixed, for which a subtractive clustering is necessary. Then both C-means and K-means clustering are exploited to determine the clusters. These clusters will be then exploited on the basis of structural and parametric identification to determine the local models that are combined, finally, to form the multimodel. The experimental study is based on MATLAB/SIMULINK environment and a DSpace scheme with DS1104 controller board. Experimental results approve that the multimodel based on K-means clustering algorithm is the most efficient.

This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM’s outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov’s based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.

This paper deals with the performance analysis of a robust neural controller applied to the DC motor for speed control. The robustness is guaranteed by the use of the Internal Model Controller (IMC). The IMC is based on the artificial Neural Network (ANN) principle. This paper proposes an adaptive neural controller methodology in the dc motor control and the results are compared with the performance of the feedforward neural controller. Simulations results and experimental set up are presented to demonstrate the robustness of the speed controller under a wide range of load of the proposed schemes in a closed loop control. The effective results show that the performance of neural network control dc motor is improved by using adaptive neural network than the feed-forward neural network.

This paper presents a control strategy for a grid connected system fed by a photovoltaic (PV) source. A two stages topology with boost converter followed by a three phase VSI (voltage source Inverter) is used for the grid interface. The boost DC converter is controlled using an Incremental conductance maximum power point tracking technique in order to feed the maximum of power to the grid in the normal grid voltage mode. In this paper, an additional dc-bus voltage regulator is added in the control of the boost for ensuring the operation of the proposed system in the case of low grid voltage or voltage sag as well as in the normal grid voltage mode. The proposed system is simulated via the MATLAB/SIMULINK and the validity of the proposed concept is confirmed and the performance of the proposed control is satisfactory.

This paper presents a control strategy for a grid interface system fed by a photovoltaic (PV) source. A two stages topology with boost converter followed by a three phase VSI (voltage source Inverter) is used for the grid interface. The boost DC converter is controlled using an Incremental conductance maximum power point tracking technique in order to feed the maximum of power to the grid and achieve fast control response to transients and changes in weather conditions. The output of the boost converter is adaptive to the variation of the grid voltages to reduce the power losses and feeding the grid with an extra power. The proposed grid connected PV system is tested considering grid voltages variations and the obtained results confirm the highest dynamic performances mainly when a sudden decrease in the grid voltages.

The aims of this paper are mainly, the modeling and simulation of a doubly fed induction generator used in converting wind energy to an electrical one. First, wind turbine modeling is studied. Second, the method of establishing the steady state control laws of a doubly fed induction generator used in wind turbine system is presented. The model is used to derive the converter control laws of the DFIG in terms of voltage and angle control on the whole operation speed range. Finally, the simulation work performed under MATLAB-SIMULINK package is used to analyze the doubly fed induction generator and illustrate the performances of the proposed analytical method.

This paper deals with performance comparison of two controllers: proportional integral (PI) and integral proportional (IP) which used for the scalar speed control of Dual three phase induction motor (DTPIM). In order to achieve high performances in term of fast dynamic speed response and best disturbance rejection, PI and IP controllers are used. The performance of the suggested scheme has been tested using Matlab/Simulink. The simulation results show that the IP controller has better performance in terms of rejection of load disturbance, while the PI controller is the best in terms of response time.

This article presents a direct control of the rotor field (DFOC) applied to the double star induction machine. The difficulty of control in the induction machine is due to the torque and flux coupling because the change in load torque inevitably induces flux variation and to ensure separation control of these two variables, field-oriented control is often used, in this reason the direct control centered on the field with the PI speed regulator is used to control this machine powered by two voltage source inverters. Then, the results obtained in the Matlab/Simulink environment have presented the performances of the system and are tested under different operating conditions.

This paper focuses on the study and design of the observer for the induction hob system. This heating process operates on the principle of electromagnetic induction. It is efficient and allows a very high temperature. Its control as well as the control of the heat transfer require knowledge of different parametres involved. However, it is difficult to give an accurate the temperature measurement of the pot bottom, due to the uncertainty of the heat transfer from the inductor to the pot, and from the pot to the temperature sensors. To give an estimate of this state, the idea is to use an auxiliary system which is the observer of Luenberger.

This paper discusses the modeling of dual three phase induction motor (DTPIM). The purpose of this paper is to study the impact of the stator mutual leakage inductance on the modeling of such machine in steady state and dynamic operation. This study incorporates two models. First, the effects of mutual leakage inductance between the two stator windings sets are simply neglected. Second, the mutual leakage inductance has been included. The two models have a good performance but, circulating harmonic currents originate from mutual leakage coupling between the stator windings appear.

The intelligent controls techniques have been an important solution for draw up the connection to the grid for the renewable energy. The power created in the system is changeable as the wind speed is uncertain the injection of this energy required the synchronization to the grid. This article proposes the active and reactive power control by the use of the intelligent technique fuzzy logic for the control of the inverter connected to the filter LCL. The control of the powers is carried out independently by the control of the direct and quadrature currents with the vector control technique. The performance of the connection to the grid with the fuzzy control designed has been analyzed through a simulation using Matlab/Simulink.

Pitch control is look as the more profitable and famous way to control the power. The control of Pitch angle is so important for regulating the wind's turbine aerodynamic torque when the wind speed is higher than the nominal speed also many values may be chosen, such as wind speed, generator speed and generator power. Many methods of pitch angle control are found and approved with simulation results under Matlab\Simulink.

Linearization by feedback, whether in the dynamic meaning of input-output or the dynamic sense of the state-input, is a very important strategy for the design of non-linear system regulators. In addition, a control via Exact State Input Linearization requires rigorous conditions as well as the determination of a suitable diffeomorphism. In fact, the main objective of this paper is the analysis and resolution of the difficulties of the Exact State Input Linearization formalism by developing an analytical approach which my lead to the pratical implementation of this formalism. Thus, the stability region is studied so as to determine the stability of such a system. These two studies will be applied to a preliminary example.

In this paper, we design exponentially convergent observer for the systems with distributed parameters. A Volterra integral transformation is used to change the coordinates of the error dynamics into exponentially stable target systems using the backstepping procedure. The gains of the observer, which are attained by solving a Partial Differential Equations (PDE) system with output injection, will guarantee the exponential convergence of the observer. What is more, the design method is applied to a SIR epidemic system to consider the sensitive population.

This paper is a comparative study of three doubly fed induction motor (DFIM) speed modeling strategies through multi-model approach based on three clustering algorithms; subtractive, C-means and K-means clustering. The comparison leads to a novel clustering strategy compound of the three clustering algorithms. The novel clustering strategy is applied to modeling the speed of the doubly fed induction motor then validated experimentally on a 1kw induction motor. The experimental study is held with the help of MATLAB/SIMULINK and a dSpace system with DS1104 controller board based on digital signal processor (DSP) TMS320F240. Simulation and experimental results approve the efficiency of the proposed approach.

Correction: The name of the third author is corrected as Sbita Lassaad which was misspelled as Sbita Lassad in the original version.

This paper presents a model of a variable speed wind turbine using a permanent magnet synchronous generator (PMSG) and proposes the control schemes. The model contains an aerodynamic part of the wind turbine, a mecanic part and an electric part. To validate the model and the proposed control schemes, simulations have been conducted with Matlab/Simulink.

This paper deals with the power optimization problem in photovoltaic generators. Indeed the nonlinear characteristic of photovoltaic generators [1], leads to search for a solution to track the maximum power provided by the PV array in order to exploit well the energy. A maximum power point tracking algorithm based on an approach called increment conductance control of photovoltaic systems is well detailed in this paper. It provides good performance against the different variation of climatic conditions and load. Simulation results prove that this algorithm can effectively attend the maximum photovoltaic power's production.

This paper is directed towards estimation for random sensor failure of networked control system (NCS) subject to the random packet dropout. In reality, certain observations may be missing. This is possibly due to network induced delay, random packet dropout, access constraints, etc. Therefore, in this work, an approach is proposed to perform estimation in random packet dropout environments. The solution is found through the use of adaptive estimation via parallel processing,in which the probability of sensor failure is either unknown or a priori known but varies due to changes in environmental conditions during operation (The Random packet dropout phenomenon). The Random packet dropout is modeled as a linear function of stochastic variable satisfying Bernoulli random binary distribution. Simulation results are given to illustrate effectiveness of the proposed method. In addition, the robustness of the proposed method is also shown through simulation.

In this paper, the high speed Photo voltaic (PV) powered permanent magnet synchronous motor (PMSM) drive is investigated. The PV electrical vehicle application is prospected, in order to highlight the irradiation effect on the PV panel feeding the PMSM. This yields to the direct effect on the inverter DC Bus behavior. This last is influencing the PMSM operating modes as a vehicle engine. The field oriented control (FOC) strategy is used to operate the motor and the field weakening method is The overall proposed control system is tested through computer simulation using MATLAB/Simulink, where the system efficiency is verified for a wide speed range and under the two DC bus voltage cases. The obtained simulations results are satisfactory and confirmed the usefulness of the overall proposed control algorithm.

In this paper a new online adjustable adaptive field weakening, based on the Bacterial foraging optimization algorithms is presented. In the traction and electric vehicle applications, the high speed mode is important. Effectively the Field weakening technique is needed. However in the environment supported by the system, many parameters can influence on the global control technique, like temperature, dust or vibration. Well, to resolve these problems a new adaptive control technique, support this variations, is presented in this paper.

A robust high speed algorithm, RHSA, applied in the field oriented vector control strategy, is presented in this paper for the PMSM sensorless control loop. The present algorithm, RHSA, is formed for online generating the direct and quadrature reference stator current. Based on the fuzzy logic controller and the field weakening principle, the RHSA is constructing. The sliding mode, based on Lyapunouv stability criterion, is used for the online speed estimation. The Robustness of the global control scheme is verified through computer simulation using matlab/Simulink software.

the present work is a contribution to improve efficiency and the robustness of a indirect field oriented of induction motor drives. The efficiency of the motor deteriorates for two reasons: The variation of rotor resistance and operation under light loads, with a constant field. For these reasons, a method is proposed to choose the optimal field to minimize total machine losses. On the other hand, to avoid the problem of rotor resistance variation, a mechanism based on the fuzzy logic is proposed to maintain the decoupling and avoid supplementary losses. An extensive simulation works yields to selected results to highlight the efficiency of proposed methods.

The interest for sensorless drives has been constantly raising during the last years. The fact that the rotor speed is not measured but estimated has several benefits. The drawbacks are lower speed range. Several estimation methods have been proposed to resolve this problem. In the conventional methods to estimate the rotor speed of the induction motor, when the estimated speed is transformed into the sampled data model using the mathematical equations, the sampled data model has a modelling error, which in turn produces an error in the speed estimation. The error included in the estimated speed is removed by the use of a low pass filter. As a result, the delay of the estimated speed occurs in transient by the use of low pass filter. This paper propose a novel closed loop rotor speed observer of the induction motor to resolve the problem of a conventional speed due to the delay of the estimated speed in low range and in field weakening regions. Besides, this paper presents a method to estimate exactly the rotor speed by using Luenberger observer. With the proposed algorithm, the accuracy of the estimated is guaranteed when the components of stator current converge to their actual one. The validity of the proposed method is verified by the simulation results.