Self-healing　control　plays　a　crucial　role　in　taking　remedial　action　to　minimize　the　adverse　impacts　of　sludge　bulking　in　wastewater　treatment　process　(WWTP).　However,　since　sludge　bulking　is　a　strong　nonlinear　and　complex　process　with　multiple　fault　conditions,　the　conventional　self-healing　control　is　difficult　to　obtain　reliable　performance.　Thus,　the　purpose　of　this　paper　is　to　design　a　broad　learning-based　self-healing　predictive　controller　(BL-SHPC)　for　sludge　bulking　in　WWTP.　The　main　innovations　of　the　proposed　controller　are　threefold.　First,　a　dynamic　fuzzy　broad　learning　system　(DFBLS)　with　an　adaptive　expansion　strategy　(AES)　is　used　to　identify　the　fault　conditions　of　sludge　bulking.　Then,　the　fault　features　of　sludge　bulking　can　be　comprehensively　extracted　with　desirable　performance.　Second,　a　prioritized　multi-objective　optimization　algorithm　(PMOOA)-based　predictive　control,　which　considers　the　objective　correlation　and　preference　of　fault　conditions,　is　presented　to　obtain　the　optimal　solutions　to　achieve　self-healing.　Then,　the　proposed　controller　can　feasibly　and　precisely　readjust　manipulated　variables　to　eliminate　the　sludge　bulking.　Third,　the　stability　of　the　developed　controller　is　proved　by　the　Lyapunov　stability　theorem.　Then,　the　stability　analysis　can　ensure　the　successful　application　of　BL-SHPC.　Finally,　the　proposed　BL-SHPC　is　tested　on　the　Benchmark　Simulation　Model　No.2　(BSM2)　to　validate　its　merits.　The　simulation　results　indicate　that　the　proposed　controller　can　obtain　superior　self-healing　ability　for　sludge　bulking　in　WWTP.　IEEE