Affected　by　multiple　operation　conditions,　wastewater　treatment　process　(WWTP)　is　a　complex　industrial　process　with　strong　nonlinearity　and　disturbance.　How　to　enhance　the　rapid　tracking　response-ability　and　robustness　of　the　controller　is　still　a　challenge　when　the　operation　conditions　change.　To　solve　this　problem,　a　type-2　fuzzy　broad　learning　controller　(T2FBLC)　is　proposed　in　this　paper.　First,　a　type-2　fuzzy　broad　learning　system　(T2FBLS)　is　constructed　in　T2FBLC　by　replacing　nodes　in　feature　window　with　a　group　of　interval　type-2　fuzzy　submodules.　Then,　the　proposed　T2FBLC　can　take　tracking　error　as　inputs　while　its　outputs　acting　on　WWTP　to　directly　obtain　a　control　law,　and　the　controller　makes　a　quick　tracking　response　in　different　operation　conditions.　Second,　the　weight　parameters　of　T2FBLC　are　adjusted　by　using　the　gradient　descent　method　to　ensure　the　control　performance.　In　this　way,　the　developed　T2FBLC　can　realize　online　learning　to　reduce　tracking　errors.　Third,　according　to　the　Lyapunov　function　theory,　the　stability　of　control　strategy　is　proved.　Finally,　benchmark　simulation　model　1　(BSM1)　is　adopted　to　verify　the　effectiveness　of　T2FBLC.　The　experimental　results　prove　the　applicability　and　superior　tracking　performance　of　the　proposed　method.　(c)　2021　Elsevier　B.V.　All　rights　reserved.