In　this　article,　we　investigate　the　self-learning　robust　control　synthesis　and　tracking　design　of　general　uncertain　dynamical　systems.　Based　on　the　adaptive　critic　learning,　the　robust　stabilization　method　is　developed　with　the　help　of　conducting　problem　transformation.　In　addition,　by　considering　the　optimal　control　solution　with　a　discounted　cost　function,　the　established　method　is　extended　to　address　the　robust　trajectory　tracking　design　problem.　The　Lyapunov　stability　analysis　is　also　conducted　for　proving　the　robustness　of　the　related　control　plants.　Finally,　the　simulation　verification　with　the　three　case　studies　is　provided　in　terms　of　robust　stabilization　and　trajectory　tracking,　respectively.