DocumentCode :
1102174
Title :
Process control and machine learning: rule-based incremental control
Author :
Luzeaux, Dominique
Author_Institution :
ETCA, Arcueil, France
Volume :
39
Issue :
6
fYear :
1994
fDate :
6/1/1994 12:00:00 AM
Firstpage :
1166
Lastpage :
1171
Abstract :
In this paper, we discuss a rule-based incremental control program which has been used for controlling a laser cutting robot and in simulation for driving a car on a track, for a car parking manoeuvre, or for parking a truck with one trailer. The core of the paper concerns a learning program, Candide, which learns to control a process without a priori knowledge about the process, by observing random initial evolutions of the process and acquiring a qualitative model. Monotonous or derivative relationships between inputs and outputs are recognized, and then a rule-based incremental controller Is deduced from this model
Keywords :
intelligent control; knowledge based systems; learning (artificial intelligence); learning systems; Candide; car driving; learning program; machine learning; process control; qualitative model; rule-based incremental control; Adaptive control; Control systems; Laser beam cutting; Machine learning; Optical control; Optimal control; Process control; Programmable control; Robot sensing systems; System identification;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/9.293176
Filename :
293176
Link To Document :
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