For　a　kind　of　continuous-time　nonlinear　systems　with　uncertainties,　a　robust　tracking　control　method　is　established　based　on　critic　learning　formulation　with　single　network.　Firstly,　an　augmented　system　consisting　of　the　tracking　error　and　the　reference　trajectory　is　established,　and　the　robust　tracking　control　problem　is　transformed　into　a　stabilization　design　problem.　By　adopting　a　cost　function　with　a　discount　factor　and　a　special　utility　term,　the　robust　stabilization　problem　is　transformed　into　an　optimal　control　problem.　Then,　the　optimal　cost　function　is　estimated　by　building　a　critic　neural　network,　and　consequently　the　optimal　tracking　control　algorithm　can　be　derived.　In　order　to　relax　the　initial　admissible　control　conditions　in　the　proposed　algorithm,　an　extra　term　is　added　to　the　weight　updating　law　of　the　critic　neural　network.　Furthermore,　the　stability　of　the　closed-loop　system　and　the　robust　tracking　performance　are　proved　using　the　Lyapunov　approach.　Finally,　the　effectiveness　and　applicability　of　the　developed　approach　are　demonstrated　via　simulation　results.　©　2023　Northeast　University.　All　rights　reserved.