Designing Decentralized controllers for distributed-air-jet MEMS-based micromanipulators by reinforcement learning.

Laëtitia Matignon (1) , Guillaume J. Laurent (1) , Nadine Le Fort - Piat (1) , Yves-André Chapuis (2)

1 FEMTO-ST - Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174)
2 InESS - Institut d'Electronique du Solide et des Systèmes

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.

Mots clés: reinforcement learning; MEMS-based actuator array; smart surface; decentralized control; distributed control; reinforcement learning.; reinforcement learning

Domaines: Micro et nanotechnologies/Microélectronique

Laëtitia Matignon, Guillaume J. Laurent, Nadine Le Fort - Piat, Yves-André Chapuis. Designing Decentralized controllers for distributed-air-jet MEMS-based micromanipulators by reinforcement learning.. Journal of Intelligent and Robotic Systems, 2010, 59 (2), pp.145-166. ⟨10.1007/s10846-010-9396-9⟩. ⟨hal-00547083⟩