After the developpement of a machine model that takes into account rotor structure
the operating and control aspects of faulty induction motors are under study
Lastly, sensorless control is also a research axis that we explore. Our aim is to build a vector control which is simple as regards the expermental implementation aspects and computer time consumption. It also has to be robust and thus relies on the knowledge of minimal parameters for powerful control. The paper [C7] shows this method and the early results. This approach has been used successfully to implement a hybrid sensorless vector control (direct and indirect) for low speed and standstill operation [D3].
Since December 2000, a new approch has been taken, which relies on the injection of a high frequency signal added to the fondamental signal of the feeding voltage (in a vector control scheme). The aim is to extract an image of the speed and position of the rotor. This is the subject of the Phd theis of Imad Al-Rouh.
We also work on parametres identification et optimisation processes using genetic algorithms and PSO (Particle Swarm Optimisation).
There are many possible control strategies with differents consequences
(dynamical behaviour, robustness...). Besides, while some give good simulation resultats,
they cannot be implanted.
With such problems, the machine models must therefore be refined and new research areas must be investigated
The studies carried out must be validated only with a test bench. This has led us to develop it.
It is the analysis of the simulation and experimental results that allowed us to tune our control algorithms and bring new ones (based on fuzzy logic and neural networks). These works have been published in international conferences and journals.
Résumé du mémoire
de Thèse [J1]
Genetic algorithms, fuzzy logic and neural networks are increasingly used in greatly varied applications. We propose to study them for the identification and the control of the induction motor. More particularly, we use the genetic algorithms in order to identify the parameters of the transient model of the machine. Controllers, based on fuzzy logic and neural networks, are implemented within a rotor-flux oriented control scheme.
The influence of variation of the parameters on the operation of the system is also investigated. Faulty induction machines are considered in the case of rotor broken bars. The diagnosis of these defects and their influence on the behaviour of the control are studied.
Lastly, an improvement of the mechanical-sensorless vector control is presented opening a new way towards "intelligent control". Thanks to the ever increasing power of processors, such techniques can be used to replace the sensor.
A simulation tool elaborated along with the thesis made it possible to develop the studies presented here. Particular care was devoted to the experimental side, the only guarantee of the feasibility and validation of the study.
Induction machine, Vector control, Fuzzy logic, Neural networks, Genetic algorithms, Identification, Fault diagnosis, Sensorless control.
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