The　strong　coupling　of　suspension　force　and　torque　in　a　single-winding　bearingless　flux-switching　permanent　magnet　motor　creates　a　complex　dynamic　model,　complicating　controller　parameter　tuning.　The　complexity　and　large　data　requirements　of　intelligent　algorithms　highlight　the　continued　relevance　of　PID　control,　which　remains　dominant　in　high　integration　and　low-cost　applications.　Consequently,　research　on　tuning　PID　controllers　is　crucial.　This　paper　proposes　a　controller　parameter　optimization　strategy　based　on　characteristic　models.　Initially,　the　principles　governing　the　generation　of　controllable　suspension　force　and　unbalanced　magnetic　pull　during　motor　operation　are　analyzed,　leading　to　the　development　of　corresponding　mechanistic　models.　Notably,　a　spatially　asymmetric　rotor　dynamic　eccentricity　modulation　operator　is　introduced　to　accurately　represent　the　establishment　of　unbalanced　magnetic　pull.　The　rotor　dynamic　model　is　then　simplified,　and　a　characteristic　model　for　the　system　is　established.　To　enhance　controller　parameter　tuning,　an　online　algorithm　for　optimization　is　proposed,　which　involves　iteratively　adjusting　the　controller　parameters　over　a　specified　period,　guided　by　the　output　error　between　the　characteristic　model　and　the　actual　system.　This　approach　facilitates　the　determination　of　optimal　controller　parameters.　Finally,　experimental　studies　are　conducted　on　the　prototype,　validating　the　effectiveness　of　the　proposed　method.　　©　2013　IEEE.