For　a　category　of　nonlinear　systems　subject　to　time-varying　constraints　on　tracking　error　and　full　states　and　unknown　control　directions,　this　article　develops　an　adaptive　neural　control　strategy　using　nonlinear　mapping　and　double-layer　constraint　structure.　Radial　basis　function　neural　network　is　applied　to　identify　the　unknown　system　dynamics.　The　dynamic　surface　control　with　less　learning　parameters　is　employed　to　eliminate　＂explosion　of　complexity＂　and　reduce　online　computation　burden.　The　Nussbaum　gain　technique　is　employed　to　deal　with　the　unknown　control　direction.　The　nonlinear　mapping　is　applied　to　ensure　the　satisfaction　of　the　multiple　constraints　on　state　variables　and　remove　feasibility　conditions　on　virtual　control　signals.　Double-layer　constraint　boundaries　are　incorporated　into　controller　design　process,　the　inside　boundaries　are　utilized　to　cope　with　the　multiple　state　constraints,　and　the　outside　boundaries　are　used　into　controller　and　adaptive　law　design.　Hence,　the　singularity　problem　caused　by　the　system　state　approaching　the　bound　boundary　is　completely　solved.　The　numerical　example　is　used　to　deduce　the　availability　of　developed　control　approach.