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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/19063
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dc.contributor.authorSukavanam, Nagarajan-
dc.contributor.authorPanwar V.-
dc.date.accessioned2020-12-03T06:29:46Z-
dc.date.available2020-12-03T06:29:46Z-
dc.date.issued2005-
dc.identifier.citationProceedings of 2nd Indian International Conference on Artificial Intelligence, IICAI 2005, (2005), 364- 383. Pune-
dc.identifier.isbn0972741216; 9780972741217-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/19063-
dc.description.abstractIn this paper the application of quadratic optimization and sliding mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed to a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The dynamic model uncertainties are compensated with a feedforward neural network. The FFNN requires no preliminary off-line training and is trained with on-line weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a two-arm robot manipulator to track a circular constrained surface while applying the desired force on the surface. Copyright © IICAI 2005.-
dc.description.sponsorshipNIA;Saint Mary's University-
dc.language.isoen_US-
dc.relation.ispartofProceedings of 2nd Indian International Conference on Artificial Intelligence, IICAI 2005-
dc.subjectBounded controls-
dc.subjectConstrained motion-
dc.subjectConstrained robots-
dc.subjectControl laws-
dc.subjectDifferential Riccati equation-
dc.subjectHamilton jacobi bellman-
dc.subjectHybrid position-
dc.subjectModel uncertainties-
dc.subjectOff-line training-
dc.subjectOptimal feedback control law-
dc.subjectOptimal position-
dc.subjectQuadratic optimization-
dc.subjectRobot manipulator-
dc.subjectSliding modes-
dc.subjectState variables-
dc.subjectState-space models-
dc.subjectTuning algorithm-
dc.subjectUnconstrained motion-
dc.subjectArtificial intelligence-
dc.subjectControl theory-
dc.subjectDynamic models-
dc.subjectFeedforward neural networks-
dc.subjectFlexible manipulators-
dc.subjectIndustrial robots-
dc.subjectModular robots-
dc.subjectRiccati equations-
dc.subjectRobot applications-
dc.subjectUncertainty analysis-
dc.subjectOptimization-
dc.titleNeural network based optimal position/force control for constrained robot manipulators-
dc.typeConference Paper-
dc.scopusid12804420600-
dc.scopusid17435058900-
dc.affiliationSukavanam, N., Department of Mathematics, Indian Institute of Technology, Roorkee 247667, India-
dc.affiliationPanwar, V., Department of Mathematics, Indian Institute of Technology, Roorkee 247667, India-
dc.description.correspondingauthorSukavanam, N.; Department of Mathematics, Indian Institute of Technology, Roorkee 247667, India; email: nsukvfma@iitr.ernet.in-
dc.identifier.conferencedetails2nd Indian International Conference on Artificial Intelligence, IICAI 2005, Pune, 20-22 December 2005-
Appears in Collections:Conference Publications [MA]

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