Publication
Feasible control of complex systems using automatic learning
Conference Article
Conference
International Conference on Informatics in Control, Automation and Robotics (ICINCO)
Edition
2nd
Pages
284-287
Doc link
http://www.icinco.org/Abstracts/2005/ICINCO2005_Abstracts.htm
File
Abstract
Robotic applications often involve dealing with complex dynamic systems. In these cases coping with control requirements with conventional techniques is hard to achieve and a big effort has to be done in the design and tuning of the control system. An alternative to conventional control techniques is the use of automatic learning systems that could learn control policies automatically, by means of the experience. But the amount of experience required in complex problems is intractable unless some generalization is performed. Many learning techniques have been proposed to deal with this challenge but the applicability of them in a complex control task is still difficult because of their bad learning convergence or insufficient generalization. In this work a new learning technique, that exploits a kind of generalization called categorization, is used in a complex control task. The results obtained show that it is possible to learn, in short time and with good convergence, a control policy that outperforms a classical PID control tuned for the specific task of controlling a manipulator with high inertia and variable load.
Categories
control theory, generalisation (artificial intelligence), intelligent robots, learning (artificial intelligence).
Author keywords
robot control, reinforcement learning, categorization
Scientific reference
A. Agostini and E. Celaya. Feasible control of complex systems using automatic learning, 2nd International Conference on Informatics in Control, Automation and Robotics, 2005, Barcelona, Espanya, pp. 284-287, 2005, INSTICC.
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