In　this　paper,　a　real-time　model　predictive　control　(RT-MPC)　based　on　self-organizing　radial　basis　function　neural　network　(SORBFNN)　is　proposed　for　nonlinear　systems.　This　RT-MPC　has　its　simplicity　in　parallelism　to　model　predictive　control　design　and　efficiency　to　deal　with　computational　complexity.　First,　a　SORBFNN　with　concurrent　structure　and　parameter　learning　is　developed　as　the　predictive　model　of　the　nonlinear　systems.　The　model　performance　can　be　significantly　improved　through　SORBFNN,　and　the　modeling　error　is　uniformly　ultimately　bounded.　Second,　a　fast　gradient　method　(GM)　is　enhanced　for　the　solution　of　optimal　control　problem.　This　proposed　GM　can　reduce　computational　cost　and　suboptimize　the　RT-MPC　online.　Then,　the　conditions　of　the　stability　analysis　and　steady-state　performance　of　the　closed-loop　systems　are　presented.　Finally,　numerical　simulations　reveal　that　the　proposed　control　gives　satisfactory　tracking　and　disturbance　rejection　performances.　Experimental　results　demonstrate　its　effectiveness.