Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC) | Feedback controller,Control theory,Computer science,Manipulator,Tracking system,Control engineering,Industrial robot,Artificial intelligence,Adaptive control,Trajectory,Robotics,Reinforcement learning |
DocType | ISSN | Citations |
Conference | 0743-1546 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yudha P. Pane | 1 | 4 | 1.57 |
| Subramanya P. Nageshrao | 2 | 45 | 4.95 |
| Robert Babuska | 3 | 2200 | 164.90 |