Process control and machine learning: rule-based incremental control

descriptionPublicationkeyboard_double_arrow_right Article 01 Jun 1994Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Automatic Control, volume 39, pages 1,166-1,171 (issn: 0018-9286,

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Authors: Dominique Luzeaux;

doi: 10.1109/9.293176

Process control and machine learning: rule-based incremental control

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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

Automated systems (robots, etc.) in control theory

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