The　high　tracking　control　precision　and　fast　finite-time　convergence　for　nonlinear　systems　is　a　significant　challenge　due　to　complex　nonlinearity　and　unknown　disturbances.　To　address　this　challenge,　a　dynamic　surface　intelligent　robust　control　strategy　with　fixed-time　sliding-mode　observer　(DSIRC-SMO)　is　proposed　to　improve　the　tracking　control　performance　in　a　finite　time.　First,　adaptive　fuzzy　neural　network　based　on　a　predictor　(P-AFNN)　is　designed　to　imitate　the　complex　nonlinearity.　In　particular,　the　weight　adaptive　law　is　formulated　by　utilizing　the　prediction　error　information,　which　improves　the　accuracy　of　approximating　the　nonlinear　system.　Second,　the　fixed-time　sliding-mode　observer　(SMO)　is　integrated　into　the　dynamic　surface　control　technique　to　deal　with　unknown　disturbances　and　modeling　errors　in　a　fixed　time.　This　integration　allows　for　timely　updates　the　dynamic　surface　using　observation　information,　thereby　enhancing　the　system＇s　anti-interference　capability.　Then,　the　fixed-time　convergence　of　SMO　is　proven.　Third,　the　proposed　DSIRC-SMO　can　be　effectively　implemented　and　the　finite-time　convergence　of　DSIRC-SMO　is　proven　in　detail　based　on　the　fixed-time　convergence　of　SMO.　Finally,　numerical　simulation　and　actual　wastewater　treatment　processes　simulation　are　conducted　to　validate　the　effectiveness　of　DSIRC-SMO.