Optimal　control　methods　have　gained　significant　attention　due　to　their　promising　performance　in　nonlinear　systems.　In　general,　an　optimal　control　method　is　regarded　as　an　optimization　process　for　solving　the　optimal　control　laws.　However,　for　uncertain　nonlinear　systems　with　complex　optimization　objectives,　the　solving　of　optimal　reference　trajectories　is　difficult　and　significant　that　might　be　ignored　to　obtain　robust　performance.　For　this　problem,　a　double-closed-loop　robust　optimal　control　(DCL-ROC)　is　proposed　to　maintain　the　optimal　control　reliability　of　uncertain　nonlinear　systems.　First,　a　double-closed-loop　scheme　is　established　to　divide　the　optimal　control　process　into　a　closed-loop　optimization　process　that　solves　optimal　reference　trajectories　and　a　closed-loop　control　process　that　solves　optimal　control　laws.　Then,　the　ability　of　the　optimal　control　method　can　be　improved　to　solve　complex　uncertain　optimization　problems.　Second,　a　closed-loop　robust　optimization　(CL-RO)　algorithm　is　developed　to　express　uncertain　optimization　objectives　as　data-driven　forms　and　adjust　optimal　reference　trajectories　in　a　close　loop.　Then,　the　optimality　of　reference　trajectories　can　be　improved　under　uncertainties.　Third,　the　optimal　reference　trajectories　are　tracked　by　an　adaptive　controller　to　derive　the　optimal　control　laws　without　certain　system　dynamics.　Then,　the　adaptivity　and　reliability　of　optimal　control　laws　can　be　improved.　The　experimental　results　demonstrate　that　the　proposed　method　can　achieve　better　performance　than　other　optimal　control　methods.