To　address　the　issue　of　low　tracking　efficiency　of　robot　fish　under　disturbance,　this　study　proposes　an　anti-disturbance　and　adaptive　error　constraint　control　scheme.　Firstly,　by　designing　a　virtual　control　input　and　updating　the　adaptive　line-of-sight　guidance　law　using　integral　link,　the　motion　position　deviation　caused　by　side　slip　is　eliminated　and　the　robot′s　anti-interference　ability　is　enhanced.　Secondly,　by　constructing　an　adaptive　controller　for　yaw　and　surge　of　the　robot　fish,　the　neural　network　function　fits　uncertainties　and　flow　disturbances　in　the　model,　compensating　for　system　control　input　with　an　approximation　value.　This　improves　the　body′s　adaptability　to　environmental　conditions.　Finally,　utilizing　obstacle　Lyapunov　theory,　consistent　final　boundedness　of　robot　fish　tracking　position　and　angle　is　proven.　Through　simulation　and　experiment,　compared　with　the　classical　guidance　scheme,　the　proposed　scheme　improves　the　tracking　efficiency　and　steady-state　performance　of　the　robot　fish,　and　the　position　error　convergence　rate　of　the　robot　fish　is　increased　by　14.57%　on　average.　©　2024　Science　Press.　All　rights　reserved.