A New Speed Control Approach of Linear Induction Motor Based on Robust RST Controller and Model Reference Adaptive System Estimator

In this paper, a new model of linear induction motor including the impact of the end-effect on the motor performances is proposed. Moreover, a new strategy of control approach based on the Field-Oriented Control (FOC) technique is suggested and investigated. The proposed approach can provide a robust control strategy and overcome the limitations imposed by FOC technique, which suffers with some drawbacks in linear induction motor (LIM) such as sensitivity to parameter variations and imperfect dynamic tracking performance. In this context, the developed technique combines the benefits provided by the both approaches polynomial (RST) regulator and Model Reference Adaptive System (MRAS) observer, in order to achieve a robust controller by minimizing the external disturbances effects and reducing the influence of parameter variations. Moreover, it is revealed that the proposed conrol approach enables  an improved  rotor speed response with a reduced number of overshoot values as function of mass variations, where the reccorded maximum overshoot value is ۸%. Besides, the devopped controller demonstrates a reducded rise and settling time values under wide applied external force conditions. This confirms that the proposed MRAS-RST technique offers a good dynamic response against the parameter variations. The accuracy and control performance of the proposed technique is checked and validated using Matlab/Simulink environment software tool. Simulation results show the effectiveness of the proposed estimator with improved better robustness for RST controller for different reference tracking and disturbance rejection parameters. These significant results make the proposed approach a promising technique dedicated to the design of high-performance controller, which is highly suitable for industrial and electrical applications.

کلیدواژه ها:

End-effect ، Field-oriented ، Control ، Model Reference Adaptive System ، RST ، Polynomial