Model　predictive　control　(MPC)　has　been　considered　as　a　promising　alternative　for　the　control　of　nonlinear　systems.　However,　this　controller　suffers　from　a　challenge　that　it　is　difficult　to　deal　with　the　complex　nonlinear　systems　with　incomplete　datasets.　To　solve　this　problem,　a　novel　MPC,　by　utilizing　knowledge-data-driven　model　(KDDM),　is　designed　and　analyzed　in　this　article.　In　comparison　with　the　existing　literatures,　this　knowledge-data-driven　MPC　(KDD-MPC)　contains　these　following　contributions.　First,　a　systematic　strategy　is　developed　to　reduce　the　online　computational　burden　of　KDD-MPC.　Therefore,　this　KDD-MPC　can　own　fast　action　to　achieve　favorable　control　performance.　Second,　the　proposed　KDDM　intends　to　not　only　make　full　use　of　limited　state　information　from　the　current　model　but　also　effectively　leverage　the　knowledge　from　the　reference　model　in　the　learning　process.　Therefore,　it　is　more　efficient　for　the　complex　nonlinear　systems　with　insufficient　data.　Third,　a　novel　transfer　learning　mechanism　is　designed　to　determine　the　optimal　control　sequence　of　KDD-MPC　with　strong　adaptability.　Therefore,　it　is　suitable　to　achieve　the　desired　control　performance　for　engineering　implementations.　Finally,　the　benchmark　problem　and　industrial　application　are　provided　to　demonstrate　the　attractiveness　and　effectiveness　of　KDD-MPC.