Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64487
Title: Distributed recurrent neural networks for cooperative control of manipulators : a game-theoretic perspective
Authors: Li, S 
He, J
Li, Y
Rafique, MU
Keywords: Distributed control
Dual neural network
Game theory
Kinematic resolution
Neural network
Recurrent neural network
Redundant manipulator
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2016, v. 28, no. 2, p. 415-426 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: This paper considers cooperative kinematic control of multiple manipulators using distributed recurrent neural networks and provides a tractable way to extend existing results on individual manipulator control using recurrent neural networks to the scenario with the coordination of multiple manipulators. The problem is formulated as a constrained game, where energy consumptions for each manipulator, saturations of control input, and the topological constraints imposed by the communication graph are considered. An implicit form of the Nash equilibrium for the game is obtained by converting the problem into its dual space. Then, a distributed dynamic controller based on recurrent neural networks is devised to drive the system toward the desired Nash equilibrium to seek the optimal solution of the cooperative control. Global stability and solution optimality of the proposed neural networks are proved in the theory. Simulations demonstrate the effectiveness of the proposed method.
URI: http://hdl.handle.net/10397/64487
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2016.2516565
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