Model　predictive　control　(MPC)　has　been　successfully　applied　to　multivariable　nonlinear　systems　with　operational　constraints.　However,　the　control　performance　of　MPC　is　hindered　by　the　different　time　scales　of　controlled　variables.　A　two-time　scale　MPC　(TTSMPC)　strategy　is　developed　to　address　the　challenge　caused　by　two-time　scale　characteristics,　and　ultimately　improve　the　control　accuracy.　Two-time　scale　models　based　on　fuzzy　neural　network　(FNN),　including　slow-sampling　FNN　(S-FNN)　with　time　scale　conversion　mechanism　and　fast-sampling　FNN　(F-FNN),　are　constructed　to　model　the　two-time　scale　nonlinear　system.　Then,　the　predicted　outputs　of　all　controlled　variables　are　obtained　at　the　fast　time　scale.　On　this　basis,　the　multiobjective　optimal　control　problem　(MOCP)　is　also　solved　at　the　fast　time　scale　to　improve　control　accuracy.　Besides,　the　corresponding　convergence　and　stability　conditions　of　TTSMPC　are　proved　in　theory.　Finally,　the　experiment　results　on　the　benchmark　example　of　wastewater　treatment　process　(WWTP)　illustrate　that　TTSMPC　can　achieve　satisfactory　operation　performance　at　the　fast　time　scale.　Note　to　Practitioners—The　motivation　of　this　paper　is　to　overcome　the　negative　impact　of　two-time　scales　on　MPC　in　terms　of　control　accuracy.　Considering　the　scenario　of　an　unknown　nonlinear　system　with　two-time　scales,　a　TTSMPC　scheme　is　put　forward　to　improve　the　control　accuracy.　The　implementation　of　TTSMPC　scheme　includes　four　steps.　First,　establish　an　FNN-based　fast　sampling　model　to　compute　the　predicted　output　of　the　fast-sampling　controlled　variable　at　the　fast　time　scale.　Second,　construct　a　S-FNN　with　time　scale　conversion　mechanism　to　calculate　the　predicted　output　of　the　slow-sampling　controlled　variable　at　the　fast　time　scale.　Third,　solve　the　MOCP　on　the　fast　time　scale　to　obtain　the　control　input　and　apply　it　to　the　controlled　system.　Fourth,　correct　the　parameters　of　S-FNN　and　F-FNN　at　the　sampling　instants　of　slow-sampling　controlled　variable　and　fast-sampling　controlled　variable,　respectively.　Finally,　experiments　on　the　benchmark　platform　of　WWTP　show　the　superiority　of　TTSMPC　in　terms　of　control　accuracy.　IEEE