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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Published in:	IEEE transaction on neural networks and learning systems Vol. 33; no. 9; pp. 4551 - 4561
Main Authors:	Peng, Guangzhu, Chen, C. L. Philip, Yang, Chenguang
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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Abstract	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.
AbstractList	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. 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.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.
Author	Chen, C. L. Philip Peng, Guangzhu Yang, Chenguang
Author_xml	– sequence: 1 givenname: Guangzhu orcidid: 0000-0003-3950-0451 surname: Peng fullname: Peng, Guangzhu email: gz.peng@qq.com organization: Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China – sequence: 2 givenname: C. L. Philip orcidid: 0000-0001-5451-7230 surname: Chen fullname: Chen, C. L. Philip email: philip.chen@ieee.org organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Chenguang orcidid: 0000-0001-5255-5559 surname: Yang fullname: Yang, Chenguang email: cyang@ieee.org organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou, China
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SubjectTerms	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
Title	Neural Networks Enhanced Optimal Admittance Control of Robot-Environment Interaction Using Reinforcement Learning
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