Various　constraints　commonly　exist　in　most　physical　systems;　however,　traditional　constraint　control　methods　consider　the　constraint　boundaries　only　relying　on　constant　or　time　variable,　which　greatly　restricts　applying　constraint　control　to　practical　systems.　To　avoid　such　conservatism,　this　study　develops　a　new　adaptive　neural　controller　for　the　nonlinear　strict-feedback　systems　subject　to　state-dependent　constraint　boundaries.　The　nonlinear　state-dependent　mapping　is　employed　in　each　step　of　backstepping　procedure,　and　the　prescribed　transient　performance　on　tracking　error　and　the　constraints　on　system　states　are　ensured　without　repeatedly　verifying　the　feasibility　conditions　on　virtual　controllers.　The　radial　basis　function　neural　network　(NN)　with　less　parameters　approach　is　introduced　as　an　identifier　to　estimate　the　unknown　system　dynamics　and　reduce　computation　burden.　For　removing　the　effect　of　unknown　control　direction,　the　Nussbaum　gain　technique　is　integrated　into　controller　design.　Based　on　the　Lyapunov　analysis,　the　developed　control　strategy　can　ensure　that　all　the　closed-loop　signals　are　bounded,　and　the　constraints　on　full　system　states　and　tracking　error　are　achieved.　The　simulation　examples　are　used　to　illustrate　the　effectiveness　of　the　developed　control　strategy.