Sciweavers

Share
JIRS
2010

Designing Decentralized Controllers for Distributed-Air-Jet MEMS-Based Micromanipulators by Reinforcement Learning

9 years 12 months ago
Designing Decentralized Controllers for Distributed-Air-Jet MEMS-Based Micromanipulators by Reinforcement Learning
Distributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems is very challenging and usual approaches for contact arrayed system don’t produce satisfactory results. In this paper, we investigate reinforcement learning control approaches in order to position and convey an object. Reinforcement learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. We show how to apply reinforcement learning in a decentralized perspective and in order to address the global-local trade-off. The simulation results demonstrate that the reinforcement learning method is a promising way to design control laws for such distributed systems. Keywords MEMS-based actuator array · smart surface · decentralized control · distributed control · reinforcement learning
Laëtitia Matignon, Guillaume J. Laurent, Nadi
Added 28 Jan 2011
Updated 28 Jan 2011
Type Journal
Year 2010
Where JIRS
Authors Laëtitia Matignon, Guillaume J. Laurent, Nadine Le Fort-Piat, Yves-André Chapuis
Comments (0)
books