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Toward human-in-the-loop PID control based on CACLA reinforcement learning
A self-tuning PID control strategy using a reinforcement learning method, called CACLA (Continuous Actor-critic Learning Automata) is proposed in this paper with the example application of humanin-the-loop physical assistive control. An advantage of using reinforcement learning is that it can be done in an online manner. Moreover, since human is a time-variant system. The demonstration also shows that the reinforcement learning framework would be beneficial to give semi-supervision signal to reinforce the positive learning performance in any time-step.
History
Publication status
- Published
File Version
- Accepted version
Journal
Proceedings of the 12th International Conference on Intelligent Robotics and ApplicationsPublisher
SpringerExternal DOI
Volume
11742Page range
605-613Event name
The 12th International Conference on Intelligent Robotics and Applications (ICIRA 2019)Event location
Shenyang, ChinaEvent type
conferenceEvent date
8 - 11 August, 2019ISBN
9783030275341Series
Lecture Notes in Computer ScienceDepartment affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2019-08-07First Open Access (FOA) Date
2020-08-02First Compliant Deposit (FCD) Date
2019-08-06Usage metrics
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