The　consensus　problem　for　a　class　of　high-order　nonlinear　multi-agent　systems　(MASs)　with　external　disturbance　and　system　uncertainty　is　studied.　We　design　an　online-update　radial　basis　function　(RBF)　neural　network　based　distributed　adaptive　control　protocol,　where　the　sliding　model　control　method　is　also　applied　to　eliminate　the　influence　of　the　external　disturbance　and　system　uncertainty.　System　consensus　is　verified　by　using　the　Lyapunov　stability　theorem,　and　sufficient　conditions　for　cooperative　uniform　ultimately　boundedness　(CUUB)　are　also　derived.　Two　simulation　examples　demonstrate　the　effectiveness　of　the　proposed　method　for　both　homogeneous　and　heterogeneous　MASs.