The　model　predictive　control　(MPC)　method　is　widely　used　in　multivariable　process　control　due　to　its　optimization　control　nature　and　easy　engineering　realization.　Aiming　at　the　large　control　error　fluctuations　caused　by　various　noises,　unmeasurable　states,　and　disturbances　under　the　MPC　method　in　practice,　this　paper　proposes　a　novel　enhanced　MPC　(En-MPC)　method　that　uses　Kalman　filter　to　estimate　unknown　states　and　combines　with　the　state　gain　matrix　for　control　compensation.　Firstly,　given　the　difficulty　of　measuring　some　key　states　of　actual　industrial　processes,　the　unknown　states　are　estimated　online　through　Kalman　filter　technology;　Secondly,　the　state　estimation　values　are　used　as　the　initial　value　of　the　prediction　model　to　obtain　the　future　output　information　of　the　system,　and　the　open-loop　optimization　solution　is　calculated　in　the　finite　horizon　by　solving　the　optimization　objective　function.　The　relationship　equation　between　the　state　variance　and　the　state　gain　matrix　is　established　and　optimized　to　obtain　the　optimal　gain　matrix,　which　is　multiplied　by　the　state　estimation　as　the　output　of　the　compensation　controller.　Finally,　the　solution　of　the　basic　optimization　controller　and　the　solution　of　the　compensator　is　added　together　to　act　as　the　final　control　input　to　the　controlled　plant.　The　upper　bounds　of　the　state　variables　of　the　proposed　method　are　proved　by　the　induction　method　in　the　root-mean-square　sense,　and　the　stability　of　the　system　under　the　algorithm　is　demonstrated.　Simulations　and　sewage　treatment　process　data　experiment　show　the　effectiveness　and　practicability　of　the　proposed　method.(c)　2022　Elsevier　Ltd.　All　rights　reserved.