| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系所 === 94 === This thesis adopts intelligent control technique to design the controller and using the torque sharing strategy to implement the intelligent indirect torque control drive system for switched reluctance motors (SRMs). The proposed control scheme can improve system response. The merits of SRMs include high torque, high efficiency, no rotor windings, and low cost. However, the structure of salient poles on both the rotor and the stator brings about high nonlinearity of the output torque and makes SRM difficult to control. Since both the Neural Network (NN) technique and the Cerebellar Model Articulation Controller (CMAC) provide a good capability to deal with nonlinear characteristics, we propose a NN-based and a CMAC-based controller respectively with the grey prediction theory and the projection algorithm used in the adaptive control theory. The proposed controller structure can on-line adjust PI parameters to make the dynamic behavior of the proposed system superior to that of the system using the conventional fix-parameter PI controller.
To verify the feasibility and practicality of the controller, a dSPACE-DS1104 platform is used to implement the proposed control scheme. From the experimental results, it is seen that the dynamic performance of the SRM driver system is improved by the proposed scheme.
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