Due　to　the　existence　of　strong　nonlinearity　and　external　disturbances,　the　controller　design　of　complex　nonlinear　systems　is　a　challenging　problem.　Therefore,　it　is　necessary　to　design　an　effective　robust　predictive　controller　for　this　issue.　In　this　paper,　based　on　a　fuzzy　neural　network,　an　iterative　learning　model　predictive　control　(FNN-ILMPC)　is　designed　for　complex　nonlinear　systems.　Firstly,　a　dynamic　linearization　technique　(DIT)　is　used　to　establish　a　data-driven　model,　which　only　relies　on　input　and　output　data.　Since　the　established　model　contains　an　unknown　disturbance　term　that　may　have　an　impact　on　the　control　performance,　an　FNN　is　used　to　evaluate　the　disturbance　so　that　the　uncertainty　of　the　system　is　captured.　Subsequently,　based　on　the　above　data-driven　model,　an　FNN-ILMPC　strategy,　considering　the　impact　of　external　disturbances,　is　developed　to　eliminate　the　influence　of　disturbances.　Then,　it　is　proved　that　the　designed　controller　can　make　both　modeling　error　and　tracking　error　decrease　gradually　and　ensure　the　closed-loop　system　stability.　Finally,　experimental　results　verify　the　effectiveness　and　superiority　of　the　designed　controller.　IEEE