Model-free　predictive　control　(MFPC)　is　developed　to　address　the　issue　of　the　weak　robustness　in　model　predictive　control　(MPC)　based　on　a　physical　model.　In　predictive　control　algorithms,　the　knowledge　of　future　values　of　the　reference　is　vital　and　this　information　is　conventionally　obtained　using　the　Lagrange　extrapolation　algorithm.　However,　during　implementation,　numerical　errors　introduced　by　inaccurate　time-shifts　compromise　the　adaptability　of　the　data-driven　model　and　its　accuracy　to　reflect　the　plant＇s　dynamics　and　operating　states.　To　overcome　these　challenges,　a　time-series-based　model-free　predictive　current　control　(MF-PCC)　is　proposed　that　employs　the　ordinary　kriging　(OK)　algorithm　in　the　continuous-control-set　(CCS)　type　and　applied　to　a　permanent　magnet　synchronous　motor　(PMSM)　driving　system　as　a　current　controller.　The　prediction　errors　for　the　time-series　model　with　the　typical　prediction　and　ultralocalized　time-series　structure　are　analyzed　in　the　CCS　type.　Based　on　this　time-series　model,　state　variables　are　predicted　by　the　time-shift　using　the　OK　algorithm,　replacing　the　Lagrange-based　approach　in　the　conventional　methods,　to　reduce　the　prediction　error.　Both　simulation　and　experimental　results　demonstrate　the　effectiveness　of　the　proposed　method,　highlighting　its　superiorities　in　current　quality　and　model　accuracy,　as　well　as　its　enhanced　robustness.