Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning

In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 33; H. 9; S. 4551 - 4561
Hauptverfasser:	Peng, Guangzhu, Chen, C. L. Philip, Yang, Chenguang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive control Admittance admittance control Artificial neural networks Control systems Cost function Electrical impedance Impedance Learning Learning systems Manipulators Neural networks neural networks (NNs) reinforcement learning (RL) Robot control robot–environment interaction Tracking Trajectory Uncertainty
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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