Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55937
Title: Dynamic neural networks for kinematic redundancy resolution of parallel stewart platforms
Authors: Mohammed, AM
Li, S 
Keywords: Stewart platform
Constrained quadratic programming
Kinematic redundancy
Recurrent neural networks
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2015, v. 46, no. 7, p. 1538-1550 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: Redundancy resolution is a critical problem in the control of parallel Stewart platform. The redundancy endows us with extra design degree to improve system performance. In this paper, the kinematic control problem of Stewart platforms is formulated to a constrained quadratic programming. The Karush-Kuhn-Tucker conditions of the problem is obtained by considering the problem in its dual space, and then a dynamic neural network is designed to solve the optimization problem recurrently. Theoretical analysis reveals the global convergence of the employed dynamic neural network to the optimal solution in terms of the defined criteria. Simulation results verify the effectiveness in the tracking control of the Stewart platform for dynamic motions.
URI: http://hdl.handle.net/10397/55937
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2015.2451213
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