Anaerobic-anoxic-oxic　(A(2)O)　reactors,　as　the　core　parts　of　wastewater　treatment　process　(WWTP),　have　attracted　considerable　attention　to　achieve　the　reliability　of　denitrification　and　dephosphorization.　However,　it　is　difficult　to　realize　the　optimal　operation　of　A(2)O　reactors　due　to　the　existence　of　nonlinear　dynamics　and　large　uncertainties.　To　solve　this　problem,　a　robust　optimal　control　(ROC)　strategy　is　developed　to　improve　the　operation　performance　of　A(2)O　reactors.　First,　data-driven　systematic　evaluation　criteria　are　developed　to　describe　the　operational　indicators　of　changeable　conditions.　Second,　a　robust　optimization　algorithm　is　designed　to　select　the　optimal　solution.　Third,　a　fuzzy　neural　network　(FNN)　is　used　to　track　the　optimal　solution　in　the　control　process.　Finally,　this　proposed　ROC　strategy　is　applied　to　the　phosphorus　removal　benchmark　simulation　model　(BSM1-P)　and　the　real　A(2)O　reactors.　The　results　demonstrate　that　the　strategy　developed　in　this　paper　has　great　potential　for　application　in　real　A(2)O　reactors.