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Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks

Authors
Journal
Neurocomputing
0925-2312
Publisher
Elsevier
Publication Date
Volume
91
Identifiers
DOI: 10.1016/j.neucom.2012.01.034
Keywords
  • Recurrent Neural Network
  • Quadratic Programming
  • Cooperative Task Execution
  • Redundant Manipulator
  • Decentralized Kinematic Control
Disciplines
  • Communication
  • Computer Science

Abstract

Abstract This paper studies the decentralized kinematic control of multiple redundant manipulators for the cooperative task execution problem. The problem is formulated as a constrained quadratic programming problem and then a recurrent neural network with independent modules is proposed to solve the problem in a distributed manner. Each module in the neural network controls a single manipulator in real time without explicit communication with others and all the modules together collectively solve the common task. The global stability of the proposed neural network and the optimality of the neural solution are proven in theory. Application orientated simulations demonstrate the effectiveness of the proposed method.

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