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This paper proposes a non-linear observer for In- duction Machine (IM) drives which takes into consideration the saturation effects. The non-linear observer is based on an original formulation of the dynamic model of the IM taking into consideration the magnetic saturation of the iron core. A Lyapunov based convergence analysis is proposed in order to suitably compute the observer gain guaranteeing the stability of the observer. The proposed non-linear observer has been tested in numerical simulation and experimentally on a suitably developed test set-up. Its behaviour has been compared to that obtained with a classic Full-Order Luenberger Observer (FOLO) in variable flux working conditions, in terms of accuracy of the amplitude and phase of both the rotor flux linkage and the stator currents space vectors. Results have shown the capability of the proposed non-linear observer to correctly estimate the rotor flux linkage amplitude and phase under flux varying conditions including strong variation of the saturation of the iron path with accuracy in the flux estimation much higher than that obtained with the classic FOLO.

In this work, we introduced a new robust hybrid control to an induction motor (IM), based on the theory of fuzzy logic and variable structure with sliding-mode control (SMC). As the variations of both control system parameters and operating conditions occur, the conventional control methods may not be satisfied further. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The combination of these two theories has given high performance and fast dynamic response with no overshoot. As it is very robust, it is insensitive to process parameters variation and external disturbances.

International audience; In this paper, a nonlinear feedback linearization approach and sliding mode controller are combined to generate the reference voltages for a matrix converter controlled by direct space vector modulation DSVM strategy fed induction motor in order to preserve the robustness with respect to desired dynamic behaviors for this drive system. The transfer function and the selection approach of the parameter for the input filter are also introduced. The simulation results of the robustness testing of the drive system have been carried out to validate the advantages of the proposed control system.

The paper presents a new method for the estimation of the electric parameters of induction motors (IMs). During the identification process the rotor flux is also estimated. The procedure relies on standstill tests performed with a standard drive architecture, hence, it is suitable for self-commissioning drives. The identification scheme is based on the model reference adaptive system (MRAS) approach. A novel parallel adaptive observer (PAO) has been designed, starting from the series-parallel Kreisselmeier observer. The most interesting features of the proposed method are the following: 1) rapidity and accuracy of the identification process; 2) low-computational burden; 3)excellent noise rejection, thanks to the adopted parallel structure; 4) avoidance of incorrect parameter estimation due to magnetic saturation phenomena, thanks to recursive rotor flux monitoring. The performances of the new scheme are shown by means of simulation and experimental tests. The estimation results are validated by comparison with a powerful batch nonlinear least square (NLS) method and by evaluating the steady-state mechanical curve of the IM used in the tests.

This paper presents improvements in Direct Torque control of an induction motor using Fuzzy logic with Fuzzy logic and neural network based duty ratio controller. The conventional DTC (CDTC) of induction motor suffers from major drawbacks like high torque and flux ripples and poor transient response. Torque and flux ripples are reduced by replacing hysteresis controller and switching table with Fuzzy logic switching controller (FDTC). In FDTC the selected switching vector is applied for the complete switching time period. The FDTC steady state performance can be improved by using duty ratio controller, the selected switching vector is applied only for the time determined by the duty ratio (δ) and for the remaining time period zero switching vector is applied. The selection of duty ratio using Fuzzy logic and neural networks is projected in this paper. The effectiveness proposed methods are evaluated using simulation by Matlab/Simulink.

The paper presents a Direct-Flux Vector Control (DFVC) scheme suitable for induction motors employed in light traction applications. In such applications, deep flux weakening is required, and the maximum torque production must be obtained under current and voltage constraints. The mathematical model of the control scheme is discussed and the implementation issues are analysed. Experimental results are presented for a small power induction motor, showing the feasibility of the proposed control, with a particular focus on the robust flux-weakening operation.

International audience; In this paper, we propose a new method for an online electric parameters identification of an induction motor (IM). This method is carried out in the IM standstill configuration and uses adaptive linear neuron (ADALINE) networks. In order to simplify the identification process, the IM model is approximated by two first-order subsystems: one is valid at low frequencies (LFs), called the slow system and the other is valid at high frequencies (HFs), called the fast system. By means of two ADALINE networks, the parameters of the slow system and the fast system are identified in LFs and HFs, respectively, and thus, the required IM parameters are derived. Finally, experimental results are presented in order to validate the proposed method and to check the accuracy of the obtained parameters. The originality of this paper is the building of a model representation that is suitable for implementation with ADALINE networks. This leads to a simple implementation and ease of parameters identification.

This paper presents a novel low-cost and simple phase-current reconstruction algorithm for three-phase induction motor (IM) under direct torque control (DTC) using the information obtained from only one shunt resistor (in series with low-side switches in a conventional three-phase inverter). The aim is to develop a low-cost high-performance IM drive. The proposed algorithm is robust and very simple. It uses the dc current to reconstruct the stator currents needed to estimate the motor flux and the electromagnetic torque. A theoretical concept is developed, the modified look-up table is presented, and current-access tables are designed and used in the phase-current reconstruction. The limitations are also studied and presented. IEEE Transactions on Power Electronics,Sept. 2012, Volume: 27, Issue: 9, pp. 4132 - 4139, disponible en ligne doi:10.1109/TPEL.2012.2190101.

in this work, we present a new strategy of direct adaptive control denoted: Extended minimal controller synthesis (EMCS). This algorithm is designed for an induction motor, which includes both electrical and mechanical dynamics under the assumptions of linear magnetic circuits. The main motivation of the EMCS control is to enhance the robustness of the MRAC algorithms, i.e. the rejection of bounded effects of rapidly varying external disturbances., {"references":["F. Naceri, L. Abida, \"A novel robust adaptive control algorithm for AC\ndrives\", Computers and Electrical Engineering Journal, 29/4, pp. 523-\n534, June 2003, Elsevier Ltd, 2003.","Chan C. C., Leung W. S., and Ng C. W, \"Adaptive Decoupling Control\nof Induction Motor\", IEEE Trans. Ind. Electronics, Vol. 37, N┬░1, pp. 41-\n47, 1990.","F. Naceri, M. Boulemden, L. Abida, \"Adaptive control for vectorcontrolled\ninduction motor\", Electromotion Journal, Vol. 8, N┬░1,\nJan/Mar. 2001, pp. 29-38.","F. Naceri, S. Belkacem, \"Improvement in DTC-SVM of AC Drives\nUsing a New Robust Adaptive Control Algorithm\", International\nJournal of Control, Automation, and Systems, vol. 9, no. 2, pp.267-275,\n2011.","S. Belkacem, F. Naceri, \" A novel robust adaptive control algorithm and\napplication to DTC-SVM AC Drivers\", Serbian journal of Electrical\nengeneering,Vol.7,May 2010, pp.21-40.","K.S. Narendra, L.S. Valavani, \"A Comparison of Lyapunov and\nHyperstability Approaches to Adaptive Control of Continuous Systems'',\nIEEE Transaction on Automatic Control, Vol.25, No. 2, Apr. 1980, pp.\n243 - 247.","F. Naceri, S. Belkacem, \"A new robust approach to MCS Algorithm.\nApplication to an AC Machines\", Archives of Electrical Engineers, Vol.\nIV, N┬░2, pp.163-178, 2006.","Dote Y, \"Servo Motor and Motion Control using Digital Processus\",\nEnglewood Diffs, NJ : Prentice Hall, 1990.","Fitzgerald A. Z., Kingsley C., and Umans S. D., \"Electric\nMachinery\", New York: MC Graw Hill, 1983.\n[10] Fu Y. Y, \"Decoupling and Adaptive Control of Three Phase Induction\nMachines\", (in French), Ph.D Thesis, Univ. Montpellier, 1991.\n[11] Krause P. C., \"Analysis of Electric Machinery\", New York: Mc Graw\nHill, 1986.\n[12] Leonard W., \"Control of Electrical Drives\", Berlin: Springer -Verlag,\n1985."]}

Yalcin, Turgay/0000-0002-6400-4786 WOS: 000291757200001 Protection of an induction motor (IM) against possible faults, such as a stator winding fault, due to thermal deterioration, rotor bar and bearing failures, is very important in environments in which it is used intensively, as in industry as an actuator. In this work, a real time digital protection algorithm based on principal component analysis (PCA) and neural network method is presented for induction motors. The proposed protection algorithm covers internal winding faults (also known as stator faults), broken rotor bar faults, and bearing faults. Many laboratory experiments have been performed on a specially designed induction motor to evaluate the performance of the suggested protection algorithm. The hybrid protection algorithm described in this paper uses is based on a three phase rms supply. These currents are first preprocessed by PCA to extract distinctive features called residuals. The calculated residuals are applied to a feed-forward back-propagation neural network as input vectors for decision making. Outputs of the network are signals denoting winding fault, rotor bar fault, bearing fault, and normal operation. The proposed algorithm is implemented by using Matlab (TM) and C++ with a NI-DAQ data acquisition board.

International audience; Vehicle emission reduction has been a research objective for many years, by improving fuel economy and energy efficiency. Therefore, this paper presents a fuzzy logic controller for a Parallel Hybrid Electric Vehicle (PHEV). The PHEV required driving torque is generated by a combined contribution from an Internal Combustion Engine (ICE) and an Induction Motor (IM). The proposed Fuzzy Logic Controller (FLC) is designed based on the desired driving torque and the batteries State of Charge (SoC) with the objective to minimize fuel consumption and emissions, while enhancing or maintaining the PHEV driving performance characteristics. The fuzzy controller output controls the ICE throttle angle degree to achieve operation in a high efficiency region. The induction motor is sized to supply peak power to meet the load power requirement of the PHEV. The proposed PHEV fuzzy controller is implemented and simulated via the advanced vehicle simulator ADVISOR using the European urban (ECE-15) and sub-urban (EUDC) driving cycles. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy, and emissions.

A novel scheme for the direct torque control (DTC) of an induction motor (IM) is proposed, which uses a single sensor of current inserted in the inverter dc link. The rationale behind the proposal is to develop a low-cost but high performance IM drive. The scheme exploits a simple and robust algorithm to reconstruct the stator currents needed to estimate the motor flux and torque. The algorithm operates in two stages: first, it predicts the stator currents from a model of the motor and then adjusts the prediction on the basis of the sensed dc-link current. Experimental results are given to demonstrate the ability of the scheme in reproducing the performance of a traditional DTC IM drive.