Modeling　compensation　of　nonlinear　friction　is　vital　to　improve　the　trajectory　tracking　performance　of　high　acceleration　motion　systems.　To　overcome　the　problem　of　inaccurate　estimation　of　the　start-stop　stage　nonlinear　friction　(including　friction　overshoot)　associated　with　the　traditional　parametric　model　for　high　acceleration　motion　systems,　this　paper　describes　a　novel　extended　parametric　model　combining　the　traditional　model　structure　with　the　extended　Stribeck　model.　The　training　data　for　identifying　the　model　parameters　are　obtained　using　the　high-precise　Iterative　Learning　Control　(ILC)　approach,　which　supplies　the　nonlinear　friction　feed-forward　compensation　data　with　limited　trajectories　in　the　workspace.　The　data　are　fitted　with　the　Levenberg-Marquardt　algorithm.　Finally,　the　proposed　model　is　validated　with　different　trajectories　on　a　high　acceleration　position　platform　driven　by　a　Voice　Coil　Motor　(VCM).　The　experimental　results　indicate　that　the　proposed　method　can　overcome　the　influence　of　nonlinear　friction　associated　with　the　traditional　model,　including　the　friction　overshoot　in　the　start-stop　stage.　Moreover,　the　accuracy　is　comparable　with　the　result　of　ILC,　but　offers　the　advantage　that　the　proposed　model　can　avoid　the　problem　of　poor　generalization　in　ILC　to　realize　the　friction　compensation　of　an　arbitrary　trajectory　in　the　workspace.　©　2018,　Science　Press.　All　right　reserved.