In thispaper,apracticalprocedureforlinearparameter-varying(LPV)modelingandidentificationof a roboticmanipulatorispresented,whichleadstoasuccessfulexperimentalimplementationofan LPV gain-scheduledcontroller.Anonlineardynamicmodelofatwo-degrees-of-freedommanipulator containingallimportanttermsisobtainedandunknownparameterswhicharerequiredtoconstructan LPV modelareidentified.Animportanttoolforobtainingamodelofcomplexitylowenoughtobe suitableforcontrollersynthesisistheprinciple-component-analysis-basedtechniqueofparameterset mapping.Sincetheresultingquasi-LPVmodelhasalargenumberofaffineschedulingparametersanda large overbounding,parametersetmappingisusedtoreduceconservatismandcomplexityin controllerdesignbyfindingtighterparameterregionswithfewerschedulingparameters.Asufficient a posteriori condition isderivedtoassessthestabilityoftheresultingclosed-loopsystem.Toevaluate the applicabilityandefficiencyoftheapproximatedmodel,apolytopicLPVgain-scheduledcontrolleris synthesizedandimplementedexperimentallyonanindustrialrobotforatrajectorytrackingtask.The experimentalresultsillustratethatthedesignedLPVcontrolleroutperformsanindependentjointPD controllerintermsoftrackingperformanceandachievesaslightlybetteraccuracythanamodel-based inverse dynamicscontroller,whilehavingasimplerstructure.Moreover,itisshownthattheLPV controllerismorerobustagainstdynamicparameteruncertainty.