Abstract
In this article we provide experimental results and evaluation of a compensation method which improves the tracking performance of a nominal feedback controller by means of reinforcement learning (RL). The compensator is based on the actor-critic scheme and it adds a correction signal to the nominal control input with the goal to improve the tracking performance using on-line learning. The algorithm has been evaluated on a 6 DOF industrial robot manipulator with the objective to accurately track different types of reference trajectories. An extensive experimental study has shown that the proposed RL-based compensation method significantly improves the performance of the nominal feedback controller.
Original language | English |
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Title of host publication | Proceedings 2016 IEEE 55th Conference on Decision and Control (CDC) |
Editors | Francesco Bullo, Christophe Prieur, Alessandro Giua |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 5819-5826 |
ISBN (Electronic) | 978-1-5090-1837-6 |
DOIs | |
Publication status | Published - 2016 |
Event | 55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016 |
Conference
Conference | 55th IEEE Conference on Decision and Control, CDC 2016 |
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Abbreviated title | CDC 2016 |
Country/Territory | United States |
City | Las Vegas |
Period | 12/12/16 → 14/12/16 |