An experimental verification of a model reference and sensitivity model-based self-learning fuzzy logic controller applied to a nonlinear servosystem

In this paper, an experimental verification of a self-learning fuzzy logic controller (SLFLC) is described. The SLFLC contains a learning algorithm that utilizes a second-order referent model and a sensitivity model. The effectiveness of the proposed controller has been tested in the position contro...

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Bibliographic Details
Published inProceedings of 12th IEEE International Symposium on Intelligent Control pp. 263 - 268
Main Authors Kovacic, Z., Balenovic, M., Bogdan, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Algorithm design and analysis
Approximation algorithms
Control system synthesis
Control systems
Error correction
Fuzzy control
Fuzzy sets
Fuzzy systems
Process control
Proportional control
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Summary:In this paper, an experimental verification of a self-learning fuzzy logic controller (SLFLC) is described. The SLFLC contains a learning algorithm that utilizes a second-order referent model and a sensitivity model. The effectiveness of the proposed controller has been tested in the position control loop of a chopper-fed DC servo system affected by fairly high static friction and by a gravitation dependent shaft load. The experimental results have proved that the SLFLC provides desired closed-loop behavior and eliminates a steady-state position error.
ISBN:9780780341166
0780341163
ISSN:2158-9860
2158-9879
DOI:10.1109/ISIC.1997.626468