Optimal　control　methods　have　attracted　much　attention　for　their　promising　performance　in　nonlinear　systems.　However,　it　is　difficult　to　achieve　satisfactory　performance　due　to　uncertain　disturbances.　To　cope　with　this　problem,　a　data-driven　robust　optimal　control　(DROC)　method　is　proposed　for　uncertain　nonlinear　systems.　The　merits　of　the　proposed　DROC　method　are　threefold:　First,　a　data-driven　evaluation　strategy　is　introduced　to　cap-ture　the　relationship　between　the　approximating　errors　and　the　control　variables.　Then,　the　control　performance　indexes　of　nonlinear　systems　can　be　established　within　uncertain　disturbances.　Second,　a　multi-objective　robust　optimization　algorithm　is　developed　with　a　coevolution　strategy.　Then,　robust　optimal　control　laws　can　be　obtained　to　improve　the　control　performance.　Third,　the　robust　boundedness　of　DROC　is　discussed　in　theory.　Then,　the　stability　of　the　control　systems　can　be　guaranteed　analytically.　Finally,　the　effec-tiveness　of　DROC　is　illustrated　with　two　multiple　input　multiple　output　second-order　non-linear　systems.　The　optimal　control　performances　are　displayed　in　experiments　to　demonstrate　the　effectiveness　of　DROC.(c)　2022　Elsevier　Inc.　All　rights　reserved.