The　control　of　the　wastewater　treatment　processes　(WWTPs)　is　considered　as　an　adaptive　robust　control　problem　due　to　the　presence　of　high　nonlinearity　and　disturbances.　In　this　article,　an　adaptive　robust　fuzzy　sliding　mode　control　(ARFSMC)　strategy　for　WWTPs　is　proposed　to　guarantee　the　operational　performance.　First,　considering　the　high　nonlinearity　of　the　WWTPs,　a　fuzzy　neural　network　(FNN)　is　employed　to　identify　the　system　dynamics.　And　the　identification　results　are　directly　used　for　controller　design.　To　address　the　effect　of　disturbances　on　identification　accuracy,　a　sliding　mode　observer　has　been　designed,　where　the　observation　error　is　utilized　to　adjust　the　adaptive　law　of　the　FNN　identifier.　Second,　a　variable　parameters　disturbance　observer　is　devised　to　accurately　estimate　time-varying　disturbances　in　the　WWTPs.　The　observer　is　used　to　build　a　dynamic　model　by　estimating　the　state,　thereby　obtaining　disturbance　as　an　additional　state.　And　the　parameters　within　the　observer　are　dynamically　adjusted　using　a　state　estimation　error-based　parameter　adjustment　law.　Third,　an　adaptive　switching　gain　sliding　mode　controller,　combined　with　the　disturbance　observer,　is　designed　to　improve　the　stability　of　the　system.　Specifically,　the　switching　gain　is　determined　by　subtracting　the　disturbance　observer　output　from　the　estimated　disturbance　upper　bound.　Finally,　the　experimental　results　on　the　BSM1　demonstrate　that　ARFSMC　has　superior　control　performance　compared　to　existing　methods.　IEEE