In　this　paper,　the　author　focuses　on　establishing　an　intelligent　critic　control　framework　with　robustness　guarantee　for　disturbed　nonlinear　systems.　Combining　the　neural　network　learning　ability　with　adaptive　critic　designs,　a　general　structure　of　intelligent　critic　control　is　developed　to　address　the　robustness　problems,　which　broadens　the　application　scope　of　adaptive　dynamic　programming　and　the　related　learning　control　methods.　First,　the　problem　transformation　is　conducted　for　changing　the　robust　stabilization　problem　into　optimal　control　design,　where　a　special　discounted　cost　function　is　well　defined.　Then,　a　recurrent　neural　network　is　constructed　to　learn　the　unknown　nominal　plant　with　stability　proof.　Moreover,　the　critic　network　implementation　is　presented　with　the　help　of　the　obtained　neural　identifier　and　the　adaptive　learning　architecture.　In　addition,　extension　discussions　and　several　simulation　examples　are　provided　to　display　the　robustness　verification　results　of　the　intelligent　critic　strategy.