Neural　network　control　has　been　developed　into　an　efficient　strategy　to　guarantee　the　safe　and　steady　operation　of　wastewater　treatment　process　(WWTP).　However,　due　to　the　complex　mechanism　and　serious　damage　of　sludge　bulking　in　WWTP,　it　is　significant　for　neural　network　control　to　achieve　the　timely　self-helaing　of　operation.　Therefore,　the　goal　of　this　paper　is　to　devise　a　knowledge-guided　adaptive　neuro-fuzzy　self-healing　control　(KG-ANFSHC)　for　sludge　bulking.　The　originality　of　KG-ANFSHC　is　threefold.　First,　a　knowledge　evaluation　strategy　is　introduced　to　consider　the　correlation　and　differentiation　between　the　normal　operation　condition　and　sludge　bulking　to　obtain　available　information.　Then,　the　proposed　strategy　can　provide　a　guide　for　control　to　take　remedial　actions.　Second,　a　KG-ANFSHC　based　on　a　knowledge　transfer　mechanism,　which　makes　full　use　of　knowledge　and　data　to　dynamically　adjust　its　parameters,　is　designed　to　eliminate　the　sludge　bulking.　Then,　KG-ANFSHC　can　timely　and　precisely　regulate　manipulated　variables　to　realize　the　self-healing　of　operation.　Third,　the　Lyapunov　stability　theorem　is　employed　to　ensure　the　stability　of　KG-ANFSHC.　Then,　the　proof　of　stability　can　assist　its　effective　application.　Finally,　the　proposed　control　is　applied　to　Benchmark　Simulation　Model　No.　2　(BSM2)　to　verify　its　advantages.　Several　results　demonstrate　that　KG-ANFSHC　can　own　satisfying　self-healing　performance　to　guarantee　the　operation　recovered　from　sludge　bulking.　IEEE