The　optimal　control　of　nonlinear　systems　is　crucial　to　improve　system　performance.　However,　the　uncertainties　of　cost　functions　and　systems　dynamics　make　it　difficult　to　solve　the　optimal　control　laws.　To　cope　with　this　problem,　a　hierarchical　learning-based　integrated　robust　optimal　control　(HL-IROC)　method　is　proposed　in　this　article.　The　merits　of　the　proposed　HL-IROC　method　are　three　aspects:　First,　a　hierarchical　learning-based　integrated　optimal　control　(HL-IOC)　scheme,　contains　a　system　dynamic　learning　layer　and　a　cost　function　learning　layer,　is　designed　to　transform　the　original　optimal　control　problem　into　an　integrated　optimization　problem　with　control　laws　as　decision　variables.　Then,　the　relationships　between　cost　functions　and　control　laws　are　captured　to　overcome　the　difficulties　brought　by　uncertainties　in　the　optimal　control　process.　Second,　a　global-local　cooperative　robust　evolutionary　optimization　(GL-CREO)　algorithm　is　proposed　to　obtain　the　optimal　control　laws.　Then,　a　global-local　robust　searching　strategy　is　employed　to　deal　with　two　types　of　uncertainties　for　improving　the　robustness　of　control　laws.　Third,　the　convergence　analysis　of　HL-IOC　and　GL-CREO　is　discussed　in　theory.　In　the　experiments,　the　effectiveness　of　HL-IROC　is　illustrated　with　a　nonlinear　system　and　a　wastewater　treatment　process.　©　2013　IEEE.