This　study　proposes　the　time-/event-triggered　adaptive　neural　control　strategies　for　the　asymptotic　tracking　problem　of　a　class　of　uncertain　nonlinear　systems　with　full-state　constraints.　First,　we　design　a　time-triggered　strategy.　The　effect　caused　by　the　residuals　of　the　estimation　via　radial　basis　function　(RBF)　neural　networks　(NNs),　and　the　reasonable　upper　bounds　on　the　first　derivative　of　the　reference　signal　and　the　derivative　of　each　virtual　control,　can　be　eliminated　by　designing　appropriate　adaptive　laws　and　utilizing　the　basic　properties　of　RBF　NNs.　Moreover,　the　construction　of　the　barrier　Lyapunov　functions　(BLFs)　in　this　work　ensures　the　compliance　of　the　full-state　constraints　and　also　holds　the　asymptotic　output　tracking　performance.　Then,　based　on　the　time-triggered　strategy,　we　further　design　a　relative　threshold　event-triggered　strategy.　The　proposed　event-triggered　adaptive　neural　controller　can　solve　the　main　control　objective　of　this　work,　that　is:　1)　the　full-state　constraint　requirements　of　the　system　are　not　violated　and　2)　the　output　signal　asymptotically　tracks　the　reference　signal.　Compared　with　the　traditional　method,　the　event-triggered　strategy　can　improve　the　utilization　of　communication　channels　and　resources　and　has　greater　practical　significance.　Finally,　an　example　of　single-link　robot　under　the　proposed　two　strategies　illustrates　the　validity　of　the　constructed　controllers.