Des nouvelles approches de commande et d’estimation non linéaires robustes dédiées aux entraînements électriques

The purpose of the research presented in this thesis is to propose a methodology for the control and observation of the induction motor (IM) based on the algorithms using the mean value theorem (MVT) and the transformation by sector non-linearity approach. In the first step, the different control techniques of electric drives were identified and analyzed. A robust state and estimation feedback control approach is then developed with variable parameters. In the field of low power, the removal of the mechanical speed sensor can be of economic interest and improve operational safety. We have presented two categories of methods that allow reconstructing and controlling the rotor speed with desired quantities under field-oriented control of the IM’s machine, the MVT observer and the robust MVT controller respectively. All the solutions have been validated by numerical simulation and affirmed by experimental tests to compare the accuracy and dynamics characteristics of the different methods with the MVT control. Finally, new robust control and estimation approaches with a novel representation for uncertain systems with varying parameters based on the MVT and sector nonlinear addressed to control the IM ‘s machine with FOC control. The results of the various simulation tests and the different experimental trials put into evidence the robustness and the success properties of the proposed algorithms. The thesis ends with a review of our contribution in terms of research

Robust Sensor Fault-Tolerant Control of Induction Motor Drive

International audienceThis paper presents an active fuzzy fault-tolerant control (FTC) strategy for induction motor that ensures the performances of the field-oriented control (FOC). In the proposed approach, a robust controller is synthesized in order to compensate for both the resistance variation, the load torque disturbance, and the sensor fault. The physical model of induction motor is approximated by the Takagi-Sugeno (T-S) fuzzy technique in the synchronous d-q rotating frame. Fuzzy descriptor observer is introduced to estimate simultaneously the system state and the sensor faults. A robust feedback state tracking control is proposed to guarantee the control performances by minimizing the effect of the load torque and the uncertainties. The proposed controller is based on a T-S reference model in which a desired trajectory has been specified. The performances of the trajectory tracking are analyzed using the Lyapunov theory and the optimization. Observer and controller gains are obtained by solving a set of LMIs constraint. To highlight the effectiveness of the proposed control simulation, results are introduced for a 1.5 KW induction motor