The　wastewater　treatment　process　(WWTP)　is　characterized　by　unknown　nonlinearity　and　external　disturbances,　which　complicates　the　tracking　control　of　dissolved　oxygen　concentration　(DOC)　within　operational　constraints.　To　address　this　issue,　a　data-driven　tube-based　robust　predictive　control　(DTRPC)　strategy　is　proposed　to　achieve　stable　tracking　control　of　DOC　and　satisfy　the　system　constraints.　First,　a　tube-based　robust　predictive　control　(TRPC)　strategy　is　designed　to　deal　with　system　constraints　and　external　disturbances.　Specifically,　a　nominal　controller　is　designed　to　ensure　that　the　nominal　output　accurately　tracks　the　set-point　under　tightened　constraints,　while　an　auxiliary　feedback　controller　is　designed　to　suppress　disturbances　and　restore　the　nominal　performance　of　the　disturbed　WWTP.　Second,　two　fuzzy　neural　network　identifiers　are　employed　to　provide　accurate　predictive　outputs　for　the　control　process,　overcoming　the　challenges　of　modeling　the　WWTP　with　strong　nonlinearity　and　unknown　dynamics.　Third,　the　generalized　multiplier　method　is　utilized　to　solve　the　constrained　optimization　problem　to　obtain　the　nominal　control　law,　and　the　gradient　descent　algorithm　is　used　to　obtain　the　auxiliary　control　law.　The　implementation　of　this　composite　controller　ensures　the　satisfaction　of　the　system　constraints　and　the　effective　suppression　of　disturbances.　Finally,　the　feasibility　and　stability　of　the　proposed　DTRPC　strategy　are　guaranteed　through　rigorous　theoretical　analysis,　and　its　effectiveness　is　demonstrated　through　the　simulations　on　the　benchmark　simulation　model　No.1.