Model　predictive　control　(MPC)　is　a　practical　method　for　addressing　control　issues　in　constrained　systems.　System　identification　and　constrained　optimization　are　two　key　problems　that　affect　MPC　performance.　In　this　work,　a　self-organizing　MPC　(SOMPC)　strategy　is　proposed　for　constrained　nonlinear　systems　with　unknown　dynamics　to　achieve　constraint　satisfaction　and　improve　control　performance.　First,　the　generalized　multiplier　method　is　introduced　into　the　MPC　framework　to　redesign　the　objective　function.　In　this　way,　the　constrained　optimal　control　problem　is　reconstructed　into　an　easily　solvable　unconstrained　optimal　problem.　Second,　a　self-organizing　fuzzy　neural　network　(SOFNN)　is　adopted　to　identify　unknown　nonlinear　system.　Then,　the　performance　of　SOFNN　is　optimized　by　parameter　updating　and　structure　self-organization　to　provide　accurate　prediction　output.　Third,　the　gradient　descent　algorithm　is　utilized　to　solve　nonlinear　optimization　problem　of　MPC　to　obtain　control　input.　To　ensure　practical　application,　the　convergence　of　SOFNN,　the　feasibility　and　stability　of　SOMPC　strategy　are　proved.　Finally,　the　proposed　SOMPC　strategy　is　demonstrated　by　a　numerical　experiment　and　an　industrial　process　control　simulation　experiment,　and　the　results　show　that　it　exhibits　outstanding　control　performance　and　constraint　satisfaction　ability.