Neural networks impedance control of robots interacting with environments
- Author(s): Yanan Li 1 ; Shuzhi Sam Ge 1, 2 ; Qun Zhang 1 ; Tong Heng Lee 3
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View affiliations
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Affiliations:
1:
Social Robotics Laboratory, Interactive Digital Media Institute, and NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119613, Singapore;
2: Robotics Institute, and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China;
3: Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077, Singapore
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Affiliations:
1:
Social Robotics Laboratory, Interactive Digital Media Institute, and NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 119613, Singapore;
- Source:
Volume 7, Issue 11,
18 July 2013,
p.
1509 – 1519
DOI: 10.1049/iet-cta.2012.1032 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received
08/01/2013,
Accepted
02/07/2013,
Revised
14/05/2013,
Published
In this study, neural networks (NN) impedance control is proposed for robot–environment interaction. Iterative learning control is developed to make the robot dynamics follow a given target impedance model. To cope with the problem of unknown robot dynamics, NN are employed such that neither the robot structure nor the physical parameters are required for the control design. The stability and performance of the resulted closed-loop system are discussed through rigorous analysis and extensive remarks. The validity and feasibility of the proposed method are verified through simulation studies.
Inspec keywords: iterative methods; learning systems; neurocontrollers; closed loop systems; robot dynamics; stability; control system synthesis; self-adjusting systems
Other keywords: NN impedance control; robot dynamics; control design; closed loop system performance; iterative learning control; stability; impedance model; robot-environment interaction; neural network impedance control
Subjects: Control system analysis and synthesis methods; Interpolation and function approximation (numerical analysis); Robotics; Robot and manipulator mechanics; Self-adjusting control systems; Stability in control theory; Neurocontrol
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