This　paper　is　concerned　with　the　asynchronous　control　problem　of　multi-rate　sampled-data　nonlinear　systems.　To　solve　this　problem,　the　data-driven　multi-model　predictive　control　(DMMPC)　strategy　is　proposed.　First,　a　multi-model　predictive　control　structure　is　designed　such　that　each　state　variable　of　the　multi-rate　sampled-data　nonlinear　system　can　be　controlled　synchronously　at　all　sampling　instants.　In　this　structure,　the　fuzzy　neural　network　(FNN)　is　introduced　to　build　the　multi-model.　Then,　the　prediction　outputs　of　each　state　variable　at　all　sampling　instants　are　obtained　to　provide　control　information　for　the　controller.　Specially,　the　objective　function　with　adaptive　weight　matrix　(AWM)　is　designed　to　reduce　the　influence　of　the　prediction　error　caused　by　nonlinear　fitting　on　control　performance.　Then,　the　optimal　control　laws　are　calculated　to　improve　the　control　precision.　Finally,　the　convergence　and　stability　of　DMMPC　are　proved　in　detail.　The　numerical　example　and　industrial　application　reveal　that　the　proposed　DMMPC　can　obtain　considerable　control　performance　for　the　multi-rate　sampled-data　nonlinear　systems.　Note　to　Practitioners-The　asynchronous　control　problem　of　the　multi-rate　sampled-data　nonlinear　system　(MRSNS)　may　degrade　the　operation　performance　of　closed-loop　system.　In　this　paper,　a　data-driven　multi-model　predictive　control　(DMMPC)　strategy　is　designed　for　MRSNS　without　mechanism　model.　The　strategy　mainly　consists　of　three　parts:　First,　a　multi-model　prediction　structure　based　on　fuzzy　neural　network　(FNN)　is　established　to　obtain　the　prediction　output　of　all　variables　at　each　sampling　point.　The　parameters　of　FNNs　are　corrected　at　each　sampling　point　to　ensure　prediction　accuracy.　Second,　an　objective　function　with　an　adaptive　weight　matrix　(AWM)　is　designed　to　compute　the　control　law,　in　which　AWM　is　used　to　reduce　the　influence　of　prediction　error　on　control　performance.　Third,　the　effectiveness　of　DMMPC　is　verified　by　a　numerical　example　and　an　industrial　application　of　the　wastewater　treatment　process.　The　experimental　results　show　that　DMMPC　can　achieve　satisfied　operation　performance　in　control　accuracy.