A Reinforcement Learning-based Adaptive Time-Delay Control and Its Application to Robot Manipulators
- Title
- A Reinforcement Learning-based Adaptive Time-Delay Control and Its Application to Robot Manipulators
- Authors
- Seungmin Baek; Jongchan Baek; Jinsuk Choi; HAN, SOOHEE
- Date Issued
- 2022-06-08
- Publisher
- IEEE
- Abstract
- This study proposes an innovative reinforcement
learning-based time-delay control (RL-TDC) scheme to provide
more intelligent, timely, and aggressive control efforts than
the existing simple-structured adaptive time-delay controls
(ATDCs) that are well-known for achieving good tracking
performances in practical applications. The proposed control
scheme adopts a state-of-the-art RL algorithm called soft actor
critic (SAC) with which the inertia gain matrix of the timedelay control is adjusted toward maximizing the expected
return obtained from tracking errors over all the future time
periods. By learning the dynamics of the robot manipulator
with a data-driven approach, and capturing its intractable and
complicated phenomena, the proposed RL-TDC is trained to
effectively suppress the inherent time delay estimation (TDE)
errors arising from time delay control, thereby ensuring the
best tracking performance within the given control capacity
limits. As expected, it is demonstrated through simulation
with a robot manipulator that the proposed RL-TDC avoids
conservative small control actions when large ones are required,
for maximizing the tracking performance. It is observed that
the stability condition is fully exploited to provide more effective
control actions.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/116843
- Article Type
- Conference
- Citation
- American Control Conference, 2022-06-08
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