| Summary: | 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.
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