As known, the main cause of the degradation in indirect rotor field oriented induction motor (IM) control (IRFOC) is the time-varying machine parameters, especially the rotor-time constant (T r ) and stator resistance (R s ), more pertinently, in cases of proportional-integral control with speed observation. In this work, a new exponential reaching law (ERL) based sliding mode control (SMC) is introduced to improve significantly the performances when compared to the conventional SMC which are well known susceptible to the annoying chattering phenomenon. So, the elimination of the chattering is achieved while simplicity and high performance speed tracking are maintained. In addition, an artificial neural network (ANN) technique is used to achieve an accurate on-line conjoint estimation of the most influent parameters on IRFOC. This technique is integrated in the adaptation mechanism of the model reference adaptive system (MRAS) in order to obtain adaptive sensorless scheme. The merits of the proposed method are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions.
Lot of works dealt with the robustness of the indirect rotor flux oriented control (IRFOC), however variation of electrical parameters of induction motor (IM), due mainly to the external condition like warming, cause the major problem of this techniques. In this work, we investigate and introduce a method called exponential reaching law (ERL) technique in the sliding mode controller to improve the control performance and remedy to the boring chattering phenomena. So the suppression of the chattering and the increasing of the reaching speed to the sliding surface is our first aim. As well, to get an adaptive sensorless scheme, a three classical proportional-integral controllers (PI) are included in the adaptation mechanism of the model reference adaptive system (MRAS) to obtain a conjoint online estimation of the rotor speed, the stator resistance (R S ) and the inverse of the rotor time constant (1/T r ) respectively. The proposed control technique is experimentally tested via the (RTI) blocks of Matlab/Simulink, and the dSPACE DS1104 card.
The most important problem in the control of induction machine (IM) is the change of its parameters, especially the stator resistance and rotor-time constant. The objective of<em> </em>this paper is to implement a new strategy in sensorless direct torque control (DTC) of an IM drive. The rotor flux based model reference adaptive system (MRAS) is used<em> </em>to estimate conjointly<em> </em>the rotor<em> </em>speed, the stator resistance and the inverse rotor time constant, the process of the estimation is performed on-line by a new MRAS-based artificial neural network (ANN) technique. Furthermore, the drive is complemented with a new exponential reaching law (ERL), based on the sliding mode control (SMC) to significantly improve the performances of the system control compared to the conventional SMC which is known to be susceptible to the annoying chattering phenomenon. An experimental investigation was carried out via the Matlab/Simulink with real time interface (RTI) and dSPACE (DS1104) board where the behavior of the proposed method was tested at different points of IM operation.
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