Identification of a Motor with Multiple Nonlinearities by Improved Genetic Algorithm

This paper presents a mathematical model that employs a new genetic algorithm for motor identification. Mechanical structures require precise motor information for high control performance. However, it is difficult to acquire accurate motor information and a genetic algorithm can be an adequate meth...

Full description

Saved in:
Bibliographic Details
Published inKnowledge-Based Intelligent Information and Engineering Systems pp. 981 - 987
Main Authors Kong, Jung-Shik, Kim, Jin-Geol
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
High performance
Genetic algorithm
Imperfect information
Angular position
System identification
Information structure
Motor control
Artificial intelligence
Modeling
Search algorithm
Online AccessGet full text

Cover

More Information
Summary:This paper presents a mathematical model that employs a new genetic algorithm for motor identification. Mechanical structures require precise motor information for high control performance. However, it is difficult to acquire accurate motor information and a genetic algorithm can be an adequate method to search unknown parameters using only angular position. The previous methods by using conventional genetic algorithms do not give the most optimal result since they cannot adjust the parameters with infinite precision. A new method is needed to identify uncertain motor information. This paper proposes a mathematical model that was searched by the newly proposed genetic algorithm. The induced motor model is verified through the real experiment.
ISBN:3540288961
9783540288961
3540288945
9783540288947
ISSN:0302-9743
1611-3349
DOI:10.1007/11553939_138