In　this　brief,　we　consider　the　problem　of　tracking　a　desired　trajectory　for　fully　actuated　autonomous　underwater　vehicle　(AUV),　in　the　presence　of　external　disturbance　and　model　errors.　Based　on　the　backstepping　method　and　Lypunov　stability　theorem,　we　introduce　the　dynamic　surface　control　(DSC)　technique　to　tackle　the　problem　of　＇explosion　of　complexity＇　which　existing　in　the　traditional　backstepping　algorithm.　Furthermore,　the　norm　of　the　ideal　weighting　vector　in　neural　network　(NN)　systems　is　considered　as　the　estimation　parameter,　such　that　only　one　parameter　is　adjusted.　The　proposed　controller　guarantees　uniform　ultimate　bounded　(UUB)　of　all　the　signals　in　the　closed-loop　system,　while　the　tracking　error　converges　to　a　small　neighborhood　of　the　origin.　Finally,　simulation　studies　are　given　to　illustrate　the　effectiveness　of　the　proposed　algorithm.　©　2013　Springer-Verlag　Berlin　Heidelberg.