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Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators
Feb 08, 2017Author：
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Title: Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators
Authors: Li, YC; Ding, GB; Zhao, B
Author Full Names: Li Yuan-chun; Ding Gui-bin; Zhao Bo
Source: JOURNAL OF CENTRAL SOUTH UNIVERSITY, 23 (11):2917-2925; 10.1007/s11771-016-3355-y NOV 2016
Language: English
Abstract: A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.
ISSN: 2095-2899
eISSN: 1993-0666
IDS Number: EF0OM
Unique ID: WOS:000390025300020
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