Full Text:   <2426>

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CLC number: TP241.2

On-line Access: 2018-12-14

Received: 2016-11-16

Revision Accepted: 2017-04-17

Crosschecked: 2018-11-27

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Jin Wang


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.11 P.1316-1327


Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters

Author(s):  Fan Xu, Jin Wang, Guo-dong Lu

Affiliation(s):  State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   dwjcom@zju.edu.cn

Key Words:  Cooperative manipulators, Neural networks, Inaccurate translational base frame, Adaptive control, Robust control, Robust control

Fan Xu, Jin Wang, Guo-dong Lu. Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(11): 1316-1327.

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journal="Frontiers of Information Technology & Electronic Engineering",
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%T Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters
%A Fan Xu
%A Jin Wang
%A Guo-dong Lu
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
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T1 - Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base frame parameters
A1 - Fan Xu
A1 - Jin Wang
A1 - Guo-dong Lu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
IS - 11
SP - 1316
EP - 1327
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601707

The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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