【正文】 于完成了整個SRD系統(tǒng)的設(shè)計。本次設(shè)計首先進行的是對開關(guān)磁阻電動機領(lǐng)域的基本了解。通過進圖書館和互聯(lián)網(wǎng)查找相關(guān)資料，對開關(guān)磁阻電動機的發(fā)展歷程有了相對比較全面的認識，同時，也對SRD系統(tǒng)的發(fā)展有了詳盡的了解。在這個基礎(chǔ)上，最后決定選用目前應用比較成熟的單片機80C196KC作為控制核心。接下來，最主要的就是對系統(tǒng)的硬件電路設(shè)計和軟件系統(tǒng)設(shè)計。設(shè)計的基礎(chǔ)是SR電動機的理想線性模型，以此為依據(jù)，再根據(jù)選用的SR電動機樣機，選用適宜的功率主電路，接著確定位置檢測電路以及電流斬波控制電路等相關(guān)電路，完成整個硬件電路。軟件系統(tǒng)是硬件可靠系統(tǒng)運行的保證，接下來通過參考相關(guān)資料，進行相關(guān)的軟件系統(tǒng)設(shè)計。整個系統(tǒng)的設(shè)計由此完成。通過這次設(shè)計過程，對所學的知識在實際中的應用有了更深入的理解。理論只有應用到實際中才能創(chuàng)造生產(chǎn)力，讓知識的力量得到真正的發(fā)揮。畢業(yè)設(shè)計對于即將畢業(yè)踏入社會的我有著重要的意義，它是我們大學生涯所學知識的總結(jié)和升華，對今后生活和學習都具有重要的指導意義。參考文獻[1]孫建忠、2005[2]，2000[3]，1999[4]，2008[5]，2001[6]張全柱、1998[7]「M」.機械工業(yè)出版社，2000[8]，[9]，2002[10]王喜蓮，2002[11]，2002[12]，2006[13]，2000[14]，2002[15]陳堅. ，2002[16]，2008[17]，1996[18]R. Vejian. Polynomial Neural Network Based Modeling of Switched Reluctance Motors. IEEE，2008[19] Xavier Rain, Micka168。el Hilairet. Comparative study of various current controllers for the switched reluctance machine. IEEE，2009[20]. Performance of 4 Phase SRM for Various Controllers and Optimized using Genetic Algorithm. IEEE，2010 附錄翻譯部分英文原文：Performance of 4 Phase SRM for Various Controllers and Optimized using Genetic AlgorithmDepartment of EEESona College of Technology, Salem, Tamil Nadu, IndiaAbstractThis paper presents the idea of using the Switched Reluctance Motor(SRM) as an alternative to previously used drives, in wide good and other industrial applications. In order to show the advantage of the SRM, the speed control of a switched reluctance motor (SRM) is designed by blending two artificial intelligence techniques, geneticalgorithms and fuzzy PI control. Here the Genetic Algorithm (GA) is used to optimize the rules of fuzzy inference system. The importance of the fuzzy PI controller is highlighted by paring the performance of various control approaches, including PI control and fuzzy control for speed control of SRM motor drive in terms of rise time, settling time, overshoot and it is optimized using GA.Keywords: Switched reluctance motor, fuzzy logic controller, PI controller, fuzzy PI controller, Genetic Algorithm.The Switched Reluctance Motor (SRM) has been increasingly used in variable speed drives [14]. It has been used for many years in various applications such as hybrid electric vehicles, aircraft starter/generator systems, washing machines, and automotive applications. In most of these applications, speed and torque control are necessary. To obtain high quality control, an accurate model of the SRM is often needed. The most important characteristic of the SRM is that the inductance of the magnetic circuit, which is a nonlinear function of the phase current and the rotor position. But, due to the fact that SRMs typically operate at high levels of saturation of the magnetic circuit to obtain high efficiency. Hence, the mapping among the SRM input variables, output variables and parameters is highly nonlinear .Many researchers have studied on SRMs concerning design and control which uses fuzzy modeling, estimation, and prediction. Analog control has been proven best control mechanism for any industrial drive. But Classical controllers (. PID controller)[67] require exact mathematical model of the systems and are very sensitive to parameter variations. Therefore, the use of classical controllers does not meet the requirements of the robust performance. The introduction of artificial intelligence (AI) has brought a new era in the industrial drive. Various AI based heuristic controls have shown a good prospect to bring robustness and adaptive nature in Constant speed variable torque or constant torque variable speed drive application. A fuzzy logic controller (FLC) [8]chooses the switching states based on a set of fuzzy variables. Each fuzzy variable has sets of variables which are characterized by expressions such as LARGE, MEDIUM and SMALL. Fuzzy Logic can be applied in the form PI controller, called as PI like FLC, where the PI controller parameters are automatically tuned for a wide range of speed demand and also for a wide change in operating condition (machine parameter, load torque). Here, the design of PI like FLC has also been tackled with genetic algorithm (GA). The genetic algorithm is based on binary genetic representation, a roulette wheel selection technique with elitist selection strategy and classic genetic operators: mutation and crossover. The phases of Fuzzy PI controller are realized with the help of standard procedures of the Fuzzy Logic Toolbox of the MATLAB/SIMULINK software. In the present work an experimental parison of PI controller, fuzzy logic based controller and fuzzy PI controller under load and no load conditions for SRM has been conducted. This paper is organized as follows. Section 2 reviews the SRM description. Section 3 discusses the fuzzy logic PI controller implementation and GA. Section 4 describes about the GA system, section 5 deals with GA based fuzzy PI controller and the results are presented in section 6, where a parison of the fuzzy logic PI controller with other controllers (FL and PI) is made and choice of controller is discussed. Conclusion is given in the last section.II .SRM MODEL SRM descriptionThis section describes the control requirement of a typical switched reluctance motor. Switched reluctance motor (SRM) drives are simpler in construction pared to induction and synchronous types of machine. Their bination with power electronic controllers may yield an economical solution. A machine configuration with eight stator pole and six rotor pole is considered as shown in figure 1. A SRM is a rotating electric motor where both stator and rotor have salient poles. The stator winding prises a set of coils, each of which is wound on one pole. The rotor is made from lamination in order to minimize the eddy current losses. The rotor tries to get to a position of minimum reluctance by aligning itself with the stator magnetic field when the stator windings are excited. Advantages of 4 Phase SRM motor are Smooth torque transition and less tor