Publication: A stepper motor design optimization using
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Date
2005-03-01
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A stepper motor design optimization using
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Abstract
In this project, the design optimization of a stepper motor is presented. In general, the area of study can be divided into motor principles and construction,
design methods, and digital control experiments. Theory is taught in classroom lectures, whereas control methods are learned primarily in laboratory situations.
Instruction on motor design, however, is usually limited to the study of motor construction, with practically no laboratory time spent on the actual fabrication of
motors. The production process, including material processing and winding, would take up too much time and expense. There is a need to fill this void in the area of small-motor design, and develop a program using Genetic Algorithms (GAs) as an approach to achieve optimization. The aim of optimum design in this project is to minimize the volume, weight and cost of stepper motor while keeping constraint variable at the desired value. In order to achieve the optimum design, Genetic Algorithms (GAs) approach has been applied. GAs approach is selected because it is a powerful and broadly applicable stochastic search and optimization techniques that works for many problems that are very difficult to solve by conventional methods. The design optimization procedure of a stepper motor is described in this project. A C++ program has been successfully developed based on the GAs by using the GAs library. This GAs library is a C++ library that contains tools and built-in components for using GAs to minimize the fitness function. In this project, the program that has been developed is run to get the optimization result with Microsoft Visual C++. In order to obtain better results from the program, some of the parameters have to be changed. These include GA parameter that is number of generation and size of population and penalty factor. From the result, it is shown that the objective function is achieved while keeping other constraint function at desired value. This project and successful results have proved the suitability of GA for design optimization of electrical equipment. It is shown that GA can be used to solve complex problems within a short period.