Serious　disturbances　in　wastewater　treatment　processes　(WWTPs)　can　degrade　control　performance　and　even　destabilize　the　system.　To　solve　this　problem,　a　self-organizing　sliding-mode　control　strategy　with　disturbance　observer　(RSOSMC)　is　proposed　to　enhance　the　robust　control　performance　of　WWTPs.　Considering　the　disturbances　in　WWTPs,　a　sliding　mode　control　scheme　is　designed,　which　includes　a　fuzzy　neural　network　identifier.　The　structural　risk　evaluation　algorithm　is　introduced　to　update　the　network　structure　so　that　the　identifier　can　maintain　a　reasonable　network　structure　even　under　the　influence　of　disturbances.　Then,　to　address　the　issue　of　inadequate　nonlinear　approximation　accuracy　caused　by　significant　disturbance　in　most　existing　methods,　a　disturbance　observer　is　employed　to　observe　the　approximation　error　and　disturbance　in　the　system.　The　observation　outcomes　are　then　utilized　as　tuning　signals　for　the　network　parameter　updating　algorithm,　which　accelerates　convergence　and　enhances　identification　accuracy.　Moreover,　the　stability　of　the　controlled　system　using　the　designed　RSOSMC　is　proved.　Finally,　simulation　experiments　conducted　on　the　BSM1　demonstrate　that　the　proposed　control　strategy　exhibits　better　robust　control　performance　compared　to　some　existing　control　strategies.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.