The　activated　sludge　method　is　one　of　the　most　commonly　used　methods　for　the　wastewater　treatment　process　(WWTP).　Among　them,　the　dissolved　oxygen　(DO)　concentration　is　a　key　factor　affecting　microbial　metabolism　and　wastewater　treatment　effectiveness.　However,　due　to　the　nonlinearity　and　dynamicity　of　WWTP,　it　is　difficult　to　precisely　control　the　DO　concentration　by　traditional　control　methods.　To　solve　this　problem,　the　online-growing　pipelined　recurrent　wavelet　neural　network　(OG-PRWNN)　control　is　proposed　to　improve　the　DO　control　accuracy.　First,　the　online　growing　mechanism　is　designed　to　adjust　the　number　of　modules　of　the　controller　by　measuring　the　control　performance.　Then,　the　structure　of　the　controller　is　automatically　determined　to　meet　the　different　operating　conditions　of　the　WWTP.　Second,　the　online　algorithm　of　parameters　incorporating　adaptive　learning　rates　is　designed　to　train　the　OG-PRWNN　to　meet　the　control　requirements.　In　addition,　the　stability　of　the　OG-PRWNN　controller　is　analyzed　by　the　Lyapunov　stability　theorem.　Finally,　the　performance　of　the　controller　is　verified　by　the　benchmark　simulation　model　of　WWTP.　Simulation　results　show　that　the　OG-PRWNN　controller　can　obtain　better　control　accuracy.