Browsing by Author "Alem, Gebreziher"

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    Adaptive Control of Multi-layer Switched Reluctance Motor
    (Addis Ababa University, 2018-12) Alem, Gebreziher; Mengesha, Mamo (PhD)
    The multi-layer switched reluctance motor (MSRM) are receiving significant attention from industries because of its simple structure, inexpensive manufacturability and reliability. In addition multi-layer switched reluctance motor receiving renewed attention as a viable candidate for various adjustable speed and high torque applications such as in the automotive, traction and aerospace industries. Simple power electronic drive circuit and fault tolerance of converter are specific advantages of multi-layer switched reluctance motor drives, but excessive torque ripple has limited its use to special applications. It is well known that controlling the current adequately can minimize the torque ripple because current is directly proportional to torque. The magnetization characteristics of the SRM is highly non-linear making the flux linkage and torque as the non-linear functions of both the current and rotor position. Establishing this high precision nonlinear mapping between current and rotor position is used to control the motor accurately for the analysis and control of any switched reluctance motor system. The generating or motoring mode of operation of the motor depends greatly on the value of rising or falling torque and hence it needs to be controlling more accurately the torque ripples for the practical applications. This thesis investigates the use of fuzzy logic controller and a hybrid intelligent system which is adaptive neuro fuzzy inference system (ANFIS) to reduce the torque ripples of multi-layer switched reluctance motors. Matlab simulink models of multi-layer switched reluctance motors with fuzzy logic controller and adaptive neuro fuzzy inference system (ANFIS) are developed to carry out simulation studies under loaded conditions. A comparison results shows that with fuzzy logic controller, the torque ripple is reduced by twenty two percent (22%) as compared to that without any controller. It is further observed that the adaptive neuro fuzzy inference system (ANFIS) controller reduces the torque ripples by twenty six percent (26%) as compared to that without any controller. This clearly shows that the torque ripple is reduced by using fuzzy logic controller as well the adaptive neuro fuzzy inference system (ANFIS). Moreover, performance of the adaptive neuro fuzzy inference system is better because it includes learning mechanism to adapt itself to new dynamic conditions. Key words: Multi-layer switched reluctance motor, fuzzy logic controller, adaptive neuro fuzzy inference system, torque ripples