首页|Researchers from University of Luxembourg Detail Findings in Robotics (Actor-critic Learning Based Pid Control for Robotic Ma- nipulators)
Researchers from University of Luxembourg Detail Findings in Robotics (Actor-critic Learning Based Pid Control for Robotic Ma- nipulators)
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NETL
NSTL
Elsevier
Investigators publish new report on Robotics. According to news reporting from Lux- embourg, Luxembourg, by NewsRx journalists, research stated, "In this paper, we propose a reinforcement learning structure for auto-tuning PID gains by solving an optimal tracking control problem for robot ma- nipulators. Capitalizing on the actor-critic framework implemented by neural networks, we achieve optimal tracking performance, estimating unknown system dynamics simultaneously." The news correspondents obtained a quote from the research from the University of Luxembourg, "The critic network is used to approximate the cost function, which serves as an indicator of control performance. With feedback from the critic, the actor network learns time-varying PID gains over time to optimize control input, thereby steering the system toward optimal performance. Furthermore, we utilize Lyapunov's direct method to demonstrate the stability of the closed-loop system. This approach provides an analytical procedure for a stable robot manipulator system to systematically adjust PID gains, bypassing the ad-hoc and painstaking process. The resultant actor-critic PID-like control exhibits stable adaptive and learning capabilities while maintaining a simple structure and inexpensive online computational demands."
LuxembourgLuxembourgEuropeEmerging TechnologiesMachine LearningRobotRoboticsRobotsUniversity of Luxembourg
NETL
NSTL
Elsevier
