| Switched reluctance motor (SRM) has only a short history of 30 years, but has already won a important position in the field of electric drive due to its good performance in speed adjustment, and has a bright future. But in performance analysis, design and control, there are still some problems to be resolved, such as the dependence on the position sensors, comparatively serious torque ripple, vibration and noise, which limit its further application. This paper developed a control system based on neural network for a 4-phase 8/6-pole SRM of 3 kW based on TMS320LF2407 DSP, which can be used for the technical research on SRM control.The thesis contains five chapters. The first chapter summarizes the development and research of switched reluctance drive (SRD), discusses the main research direction, and explains the main work in this paper. In chapter 2, hardware system which includes the design of power converter and various control circuit is discussed. TMS320LF2407 DSP is used to design the hardware circuits of SRM control system, and design details including the current detection, position sensing, fault protection, speed detecting, keyboard and display etc. are provided. Because of the full use of the abundant peripheral resources of DSP, it comes to the aim simplifying the circuit structure and heightening the reliability. In chapter 3, the application of neural network on SRM control system is introduced, a new the application of neural network and the method of sampling is proposed and a simulation system based on Matlab/Simulink is established. Chapter 4 discusses the routine designing issue. Because the modularized programming method is adopted, and multi-interrupt processing technique is used, operation efficiency of the control software is highly raised. At last, the foregoing SRM control system is tested. Speed adjustment is realized, and other targets on the research and design of SRM control system are reached, which establishes a good foundation for further research. |
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