Authors:
Jérémy Boes
;
Frédéric Migeon
and
François Gatto
Affiliation:
Université Paul Sabatier, France
Keyword(s):
Multi-Agent Systems, Self-organization, Intelligent Control Systems.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Real-Time Systems Control
;
Signal Processing, Sensors, Systems Modeling and Control
;
Software Agents for Intelligent Control Systems
Abstract:
Controlling heat engines imposes to deal with high dynamics, non-linearity and multiple interdependencies. A way handle these difficulties is enable the controller to learn how the engine behaves, hence avoiding the costly use of an explicit model of the process. Adaptive Multi-Agent Systems (AMAS) are able to learn and
to adapt themselves to their environment thanks to the cooperative self-organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control, reuniting learning, adaptivity, robustness and genericity. In this paper, we present an AMAS for the control of heat engines and show several results.