Bi-criteria optimal control of redundant robot manipulators using LVI-based primal-dual neural network

Cai, B; Zhang, Y

Bi-criteria optimal control of redundant robot manipulators using LVI-based primal-dual neural network

Cai, B Zhang, Y

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Publisher:: John Wiley and Sons
Publication Type:: Journal Article
Citation:: Optimal Control Applications and Methods, 2010, 31 (3), pp. 213 - 229
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Cai, B	en_US
dc.contributor.author	Zhang, Y	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Optimal Control Applications and Methods, 2010, 31 (3), pp. 213 - 229	en_US
dc.identifier.issn	0143-2087	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15397
dc.description.abstract	In this paper, a bi-criteria weighting scheme is proposed for the optimal motion control of redundant robot manipulators. To diminish the discontinuity phenomenon of pure infinity-norm velocity minimization (INVM) scheme, the proposed bi-criteria redundancy-resolution scheme combines the minimum kinetic energy scheme and the INVM scheme via a weighting factor. Joint physical limits such as joint limits and joint-velocity limits could also be incorporated simultaneously into the scheme formulation. The optimal kinematic control scheme can be reformulated finally as a quadratic programming (QP) problem. As the real-time QP solver, a primal-dual neural network (PDNN) based on linear variational inequalities (LVI) is developed as well with a simple piecewise-linear structure and global exponential convergence to optimal solutions. Since the LVI-based PDNN is matrix-inversion free, it has higher computational efficiency in comparison with dual neural networks. Computer simulations performed based on the PUMA560 manipulator illustrate the validity and advantages of such a bi-criteria neural optimal motion-control scheme for redundant robots	en_US
dc.publisher	John Wiley and Sons	en_US
dc.relation.ispartof	Optimal Control Applications and Methods	en_US
dc.relation.isbasedon	10.1002/oca.897	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Bi-criteria optimal control of redundant robot manipulators using LVI-based primal-dual neural network	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	31	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
dc.location.activity	Cairns, AUSTRALIA
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	3	en_US
pubs.volume	31	en_US

Abstract:

In this paper, a bi-criteria weighting scheme is proposed for the optimal motion control of redundant robot manipulators. To diminish the discontinuity phenomenon of pure infinity-norm velocity minimization (INVM) scheme, the proposed bi-criteria redundancy-resolution scheme combines the minimum kinetic energy scheme and the INVM scheme via a weighting factor. Joint physical limits such as joint limits and joint-velocity limits could also be incorporated simultaneously into the scheme formulation. The optimal kinematic control scheme can be reformulated finally as a quadratic programming (QP) problem. As the real-time QP solver, a primal-dual neural network (PDNN) based on linear variational inequalities (LVI) is developed as well with a simple piecewise-linear structure and global exponential convergence to optimal solutions. Since the LVI-based PDNN is matrix-inversion free, it has higher computational efficiency in comparison with dual neural networks. Computer simulations performed based on the PUMA560 manipulator illustrate the validity and advantages of such a bi-criteria neural optimal motion-control scheme for redundant robots

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http://hdl.handle.net/10453/15397