Self-Organizing Agents for an Adaptive Control of Heat Engines

• Published in: Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics
• Main Authors: Boes, Jérémy, Migeon, Frédéric, Gatto, François
• Other Authors: Systèmes Multi-Agents Coopératifs (IRIT-SMAC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2013
• Subjects: Multi-agent systems; Self-organization; Intelligent control systems; [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]; Iceland
• Online Access: https://hal.science/hal-04083843; https://hal.science/hal-04083843/document; https://hal.science/hal-04083843/file/boes_12476.pdf; https://doi.org/10.5220/0004483302430250

International audience 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.