The application of fuzzy logic in automatic modelling of electromechanical systems

Electromechanical systems are usually modelled using energy conversion theory. However, this representation is not accurate enough. The reasons are the presence of non-linear relations between the variables, changes in system parameters, and the difficulty encountered sometimes in taking into accoun...

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Veröffentlicht in:Fuzzy sets and systems Jg. 95; H. 3; S. 273 - 293
Hauptverfasser: Costa Branco, P.J., Dente, J.A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.05.1998
Elsevier
Schlagworte:
Applied sciences
Approximate reasoning
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
General logic
Linguistic modelling
Logic and foundations
Machine learning
Mathematical logic, foundations, set theory
Mathematics
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Sciences and techniques of general use
Linguistic modelling
Approximate reasoning
Pattern recognition
Machine learning
Fuzzy logic
Fuzzy algorithm
Automatic system
Linguistic model
Learning algorithm
Electromechanical system
ISSN:0165-0114, 1872-6801
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Zusammenfassung:Electromechanical systems are usually modelled using energy conversion theory. However, this representation is not accurate enough. The reasons are the presence of non-linear relations between the variables, changes in system parameters, and the difficulty encountered sometimes in taking into account, in a simple and precise way, physical phenomena like, friction, viscosity, and saturation. So, it is useful to automatically extract the relations that represent the system behaviour. We investigate in this paper three fuzzy learning algorithms which represent the development of our study and are used for automatic modelling of electromechanical systems. We begin with a very simple algorithm. Some problems are pointed as containing the requisites to be a good model; next, two methods which are composed of a fuzzy-cluster-based algorithm and a fuzzy-supervised-learning algorithm are employed. We explore their learning capabilities in situations like modelling in a direct and inverse way, the amount of information necessary to build a good model, and the problem of selecting the information relevant to the learning process. The algorithms are analysed in an experimental system in our laboratory. We close with a simple control application for the relationship between fuzzy modelling and electromechanical systems designing a feedforward learning controller for the experimental system.
ISSN:0165-0114
1872-6801
DOI:10.1016/S0165-0114(96)00265-5