Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/62045
DC FieldValue
dc.titleDirect adaptive neural network control of robots
dc.contributor.authorGe, S.S.
dc.contributor.authorHang, C.C.
dc.date.accessioned2014-06-17T06:46:50Z
dc.date.available2014-06-17T06:46:50Z
dc.date.issued1996
dc.identifier.citationGe, S.S.,Hang, C.C. (1996). Direct adaptive neural network control of robots. International Journal of Systems Science 27 (6) : 533-542. ScholarBank@NUS Repository.
dc.identifier.issn00207721
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62045
dc.description.abstractNeural network modelling of robots is introduced using the GL matrices and operator (Ge et al. 1994), and a new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques, and there is no need for matrix inversion. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required and no time-consuming training process is necessary, except for initializing the neural networks based on approximate parameters of the initial posture at time t = 0. It is shown that if gaussian radial basis function networks are used, uniformly stable adaptation is assured and asymptotic tracking is achieved.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleInternational Journal of Systems Science
dc.description.volume27
dc.description.issue6
dc.description.page533-542
dc.description.codenIJSYA
dc.identifier.isiutNOT_IN_WOS
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