Dec 5, 2022 · This paper presents a design of an adaptive PID gain tuning based on deep deterministic policy gradient reinforcement learning agent for PID computed-torque ...
Oct 22, 2024 · Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement ...
Abstract: This paper presents a design of an adaptive PID gain tuning based on deep deterministic policy gradient reinforcement learning agent for PID ...
Abstract: This paper presents a design of an adaptive PID gain tuning based on deep deterministic policy gradient reinforcement learning agent for PID computed- ...
The PID deterministic policy µ, so that. Adaptive PID computed-torque control of robot manipulators based on DDPG reinforcement learning 177. at = μ ( st ) ...
Akram Ghediri [15] proposed an adaptive PID gain tuning design based on a deep deterministic policy gradient reinforcement learning agent for the PID computed ...
Apr 25, 2024 · Adaptive PID computed-torque control of robot manipulators based on DDPG reinforcement learning. Int. J. Model. Identif. Control. 41(3): 173 ...
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Aug 5, 2024 · Although modern control theory has made great progress, classical algorithms such as PID are still used in robotic manipulator controllers ...
This work concentrates on learning to control a complex model of the Stewart platform using state-of-the-art deep reinforcement learning (DRL) algorithms.
This research introduces a robust control design for multibody robot systems, incorporating sliding mode control (SMC) for robustness against uncertainties ...