To　improve　the　control　performance　and　operational　performance　of　complex　nonlinear　systems　with　unknown　disturbances,　an　optimal　control　method　for　complex　nonlinear　systems　based　on　disturbance　observer　(IOCM)is　proposed.　In　order　to　obtain　a　more　accurate　system　prediction　model,　a　model　approximator　based　on　fuzzy　neural　networks　is　designed　to　capture　the　nonlinear　dynamics,　and　a　disturbance　observer　is　used　to　describe　the　unknown　disturbances.　Then,　within　the　framework　of　multi-objective　model　predictive　control,　an　optimal　control　structure　with　collaborative　cost　function　and　multi-gradient　algorithm　is　proposed　to　comprehensively　solve　set-points　and　control　laws.　The　effectiveness　of　the　method　is　verified　using　benchmark　simulation　model　1　(BSM1)of　municipal　wastewater　treatment　process.　Experimental　results　show　that　the　average　effluent　quality　(EQ)is　6　711　mg/L,　and　the　average　operating　energy　consumption　(EC)is　3　805　kW·h　under　rainstorm　weather　conditions.　Compared　with　other　step-by-step　optimal　control　methods,　IOCM　has　better　robustness　and　can　improve　the　optimization　control　performance　of　nonlinear　systems.　©　2024　Southeast　University.　All　rights　reserved.