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dc.contributor.authorLiaw, DCen_US
dc.contributor.authorHuang, JTen_US
dc.date.accessioned2014-12-08T15:47:35Z-
dc.date.available2014-12-08T15:47:35Z-
dc.date.issued1998-10-01en_US
dc.identifier.issn0921-0296en_US
dc.identifier.urihttp://hdl.handle.net/11536/31847-
dc.description.abstractIn this paper, the issues of contact friction compensation for constrained robots are presented. The proposed design consists of two loops. The inner loop is for the inverse dynamics control which linearizes the system by canceling nonlinear dynamics, while the outer loop is for friction compensation. Although various models of friction have been proposed in many engineering applications, frictional force can be modeled by the Coulomb friction pins the viscous force. Based on such a model, an on-line genetic algorithm is proposed to learn the friction coefficients for friction model. The friction compensation control input is also implemented in terms of the friction coefficients to cancel the effect of unknown friction. By the guidance of the fitness function, the genetic learning algorithm searches for the best-fit value in a way like the natural surviving laws. Simulation results demonstrate that the proposed on-line genetic algorithm can achieve good friction compensation even under the conditions of measurement noise and system uncertainty. Moreover, the proposed control scheme is also found to be feasible for friction compensation of friction model with Stribeck effect and position-dependent friction model.en_US
dc.language.isoen_USen_US
dc.subjectconstrained robotsen_US
dc.subjectgenetic algorithmsen_US
dc.subjectlearningen_US
dc.subjectfriction compensationen_US
dc.titleContact friction compensation for robots using genetic learning algorithmsen_US
dc.typeArticleen_US
dc.identifier.journalJOURNAL OF INTELLIGENT & ROBOTIC SYSTEMSen_US
dc.citation.volume23en_US
dc.citation.issue2-4en_US
dc.citation.spage331en_US
dc.citation.epage349en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
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