Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79223
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorZhang, YY-
dc.creatorChen, SY-
dc.creatorLi, S-
dc.creatorZhang, ZJ-
dc.date.accessioned2018-11-05T01:45:02Z-
dc.date.available2018-11-05T01:45:02Z-
dc.identifier.issn0278-0046-
dc.identifier.urihttp://hdl.handle.net/10397/79223-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.subjectKinematic controlen_US
dc.subjectManipulatoren_US
dc.subjectQuadratic program (QP)en_US
dc.subjectRedundancy resolutionen_US
dc.subjectUnknown physical parametersen_US
dc.titleAdaptive projection neural network for kinematic control of redundant manipulators with unknown physical parametersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage4909-
dc.identifier.epage4920-
dc.identifier.volume65-
dc.identifier.issue6-
dc.identifier.doi10.1109/TIE.2017.2774720-
dcterms.abstractRedundancy resolution is of great importance in the control of manipulators. Among the existing results for handling this issue, the quadratic program approaches, which are capable of optimizing performance indices subject to physical constraints, are widely used. However, the existing quadratic program approaches require exactly knowing all the physical parameters of manipulators, the condition of which may not hold in some practical applications. This fact motivates us to consider the application of adaptive control techniques for simultaneous parameter identification and neural control. However, the inherent nonlinearity and nonsmoothness of the neural model prohibits direct applications of adaptive control to this model and there has been no existing result on adaptive control of robotic arms using projection neural network (PNN) approaches with parameter convergence. Different from conventional treatments in joint angle space, we investigate the problem from the joint speed space and decouple the nonlinear part of the Jacobian matrix from the structural parameters that need to be learnt. Based on the new representation, we establish the first adaptive PNN with online learning for the redundancy resolution of manipulators with unknown physical parameters, which tackles the dilemmas in existing methods. The proposed method is capable of simultaneously optimizing performance indices subject to physical constraints and handling parameter uncertainty. Theoretical results are presented to guarantee the performance of the proposed neural network. Besides, simulations based on a PUMA 560 manipulator with unknown physical parameters together with the comparison with an existing PNN substantiate the efficacy and superiority of the proposed neural network, and verify the theoretical results.-
dcterms.bibliographicCitationIEEE transactions on industrial electronics, June 2018, v. 65, no. 6, p. 4909-4920-
dcterms.isPartOfIEEE transactions on industrial electronics-
dcterms.issued2018-
dc.identifier.isiWOS:000425618900047-
dc.identifier.eissn1557-9948-
dc.description.validate201810 bcrc-
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