الفهرس | Only 14 pages are availabe for public view |
Abstract Due to the advances on sensors, power electronics and signal processing, switched reluctance motor has gained a lot of commercial and academic interest. Switched Reluctance Motor (SRM) construction simplicity makes it inexpensive, in addition to its reliability, high speed capability, cooling, ruggedness and high torque to inertia ratio makes it a superior choice in different applications. However, acoustic noise and excessive torque ripple, especially at low speeds prevented SRM from widespread use. Actually there are two approaches to reduce torque ripples; one of them is to improve the magnetic design of the motor, while the other is to use sophisticated electronic control. This thesis combines these two approaches to reduce torque ripples of switched reluctance motor through building two multi-layered switched reluctance motor (MSRM), the first one is Doublelayer switched reluctance motor (DLSRM) controlled by a hybrid intelligent system known as adaptive neuro-fuzzy inference system . And also building triple layer switched reluctance motor (TLSRM) controlled by fuzzy logic controller. Both models of DLSRM and TLSRM was built using MATLAB /SIMULINK. For comparison we also build a model of SRM of the same size as DLSRM, so according to these models, different controller were used to control DLSRM ,TLSRM and SRM the first one is proportional integral controller and the second intelligent controller adaptive neuro-fuzzy inference system Then fuzzy logic controller. The simulation results of DLSRM compared with single layer switched reluctance motor for both PI, FLC and ANFIS controllers show improvement in behavior of DLSRM controlled by ANFIS through reduction in speed settling time as well as torque ripples. |