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dc.contributor.authorRahimi, H.N.
dc.contributor.authorHoward, Ian
dc.contributor.authorCui, Lei
dc.date.accessioned2018-05-14T06:08:42Z
dc.date.available2018-05-14T06:08:42Z
dc.date.created2018-05-13T00:32:00Z
dc.date.issued2018
dc.identifier.citationRahimi, H.N. and Howard, I. and Cui, L. 2018. Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints. Mechanical Systems and Signal Processing. 112: pp. 44-60.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/66628
dc.identifier.doi10.1016/j.ymssp.2018.03.042
dc.description.abstract

This paper presents a control design for a robotic manipulator with uncertainties in both actuator dynamics and manipulator dynamics subject to asymmetric time-varying joint space constraints. Tangent-type time-varying barrier Lyapunov functionals (tvBLFs) are constructed to ensure no constraint violation and to remove the need for transforming the original constrained system into an equivalent unconstrained one. Adaptive Neural Networks (NNs) are proposed to handle uncertainties in manipulator dynamics and actuator dynamics in addition to the unknown disturbances. Proper input saturation is employed, and it is proved that under the proposed method the stability and semi-global uniform ultimate boundedness of the closed-loop system can be achieved without violation of constraints. The effectiveness of the theoretical developments is verified through numerical simulations.

dc.publisherAcademic Press
dc.titleNeural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints
dc.typeJournal Article
dcterms.source.volume112
dcterms.source.startPage44
dcterms.source.endPage60
dcterms.source.issn0888-3270
dcterms.source.titleMechanical Systems and Signal Processing
curtin.departmentSchool of Civil and Mechanical Engineering (CME)
curtin.accessStatusFulltext not available
dc.date.embargoEnd2020-04-22


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