Inteligencia Artificial aplicada a Motores DC.
- Authors
- Flores Moran, Manuel Eduardo
- Format
- MasterThesis
- Status
- publishedVersion
- Description
Technological evolution provides a range of techniques for controlling DC and AC motors. In order to determine the precise controller for speed and position motors, Overshoot percentage, Rise Time and Steady State Error are essentials parameters for analysing the purpose. Proportional Integrative and Derivative (PID) controller offers an acceptable responses, nevertheless it does not represent the optimal one because controller is unsuccessful to prevent the overshoot and reaches the reference value when a torque load disturbance is applied. Fuzzy Logic Controller (FLC) implements the human reasoning for obtaining an optimal response, the representation of this reasoning contributes to achieve an acceptable response however this response presents a significant overshoot percentage and a steady state error. Consequently with these disadvantages, many researchers have developed a controller system that links the benefits for programing the Fuzzy Logic Controller and the easy way for tuning the PID coefficients, defining as supervisor to the FLC to provide the coefficients to the PID, as a result the system offers a response according to the required specifications. This structure provides a response capable to support the noise effects and changing of parameters. In order to find the finest coefficients for the controllers, Genetic Algorithm (GA) is a global optimizer based on natural selection that obtains the controller coefficients to provide an optimal response. The purpose of this examination is to analyse the behaviour of the controllers previously mentioned and determine the efficacy of all of them. This information develops a comparison for all the controller in order to determine the appropriate according to the specifications requested.
- Publication Year
- 2015
- Language
- eng
- Topic
- VELOCIDAD
FRUZZY LOGIC
INTELIGENCIA ARTIFICIAL
MOTORES DC
- Repository
- Repositorio SENESCYT
- Rights
- openAccess
- License