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Global stability of linearizing control with a new robust nonlinear observer of the induction motor

100%
Chenafa M., Mansouri A., Bouhenna A., Etien E., Belaidi A., Denai M. A.
International Journal of Applied Mathematics and Computer Science
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2005
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tom 15
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nr 2
235-243
EN
This paper mainly deals with the design of an advanced control law with an observer for a special class of nonlinear systems. We design an observer with a gain as a function of speed. We study the solution to the output feedback torque and rotor flux-tracking problem for an induction motor model given in the natural frame. We propose a new robust nonlinear observer and prove the global stability of the interlaced controller-observer system. The control algorithm is studied through simulations and applied in many configurations (various set points, flux and speed profiles and torque disturbances), and is shown to be very efficient.
2
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Powerful nonlinear observer associated with field-oriented control of an induction motor

100%
Mansouri A., Chenafa M., Bouhenna A., Etien E.
International Journal of Applied Mathematics and Computer Science
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2004
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tom 14
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nr 2
209-220
EN
In this paper, we associate field-oriented control with a powerful nonlinear robust flux observer for an induction motor to show the improvement made by this observer compared with the open-loop and classical estimator used in this type of control. We implement this design strategy through an extension of a special class of nonlinear multivariable systems satisfying some regularity assumptions. We show by an extensive study that this observer is completely satisfactory at low and nominal speeds and it is not sensitive to disturbances and parametric errors. It is robust to changes in load torque, rotational speed and rotor resistance. The method achieves a good performance with only one easier gain tuning obtained from an algebraic Lyapunov equation. Finally, we present results and simulations with concluding remarks on the advantages and perspectives for the observer proposed with the field-oriented control.
3
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Control of an induction motor using sliding mode linearization

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Etien E., Cauet S., Rambault L., Champenois G.
International Journal of Applied Mathematics and Computer Science
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2002
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tom 12
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nr 4
523-531
EN
Nonlinear control of the squirrel induction motor is designed using sliding mode theory. The developed approach leads to the design of a sliding mode controller in order to linearize the behaviour of an induction motor. The second problem described in the paper is decoupling between two physical outputs: the rotor speed and the rotor flux modulus. The sliding mode tools allow us to separate the control from these two outputs. To take account of parametric variations, a model-based approach is used to improve the robustness of the control law despite these perturbations. Experimental results obtained with a laboratory setup illustrate the good performance of this technique.
4
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Fuzzy and neural control of an induction motor

88%
Denai M. A., Attia S. A.
International Journal of Applied Mathematics and Computer Science
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2002
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tom 12
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nr 2
221-233
EN
This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.
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