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
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Zusammenfassung: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. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3057958