In　this　paper,　we　consider　rank　estimation　for　partial　functional　linear　regression　models　based　on　functional　principal　component　analysis.　The　proposed　rank-based　method　is　robust　to　outliers　in　the　errors　and　highly　efficient　under　a　wide　range　of　error　distributions.　The　asymptotic　properties　of　the　resulting　estimators　are　established　under　some　regularity　conditions.　A　simulation　study　conducted　to　investigate　the　finite　sample　performance　of　the　proposed　estimators　shows　that　the　proposed　rank　method　performs　well.　Furthermore,　the　proposed　methodology　is　illustrated　by　means　of　an　analysis　of　the　Berkeley　growth　data.