Municipal　solid　waste　incineration　(MSWI)　is　one　of　the　main　means　to　dispose　of　municipal　solid　waste　(MSW).　MSW　in　China　has　a　wide　range　of　sources,　complex　components,　and　large　fluctuations　in　calorific　value.　Its　incineration　process　usually　relies　on　manual　intervention.　This　will　lead　to　a　low　degree　of　intelligence　in　the　MSWI　process　and　it　is　difficult　to　meet　the　increasing　control　requirements.　MSWI　has　many　uncertain　characteristics　such　as　multivariable　coupling　and　working　condition　drift,　so　it　is　difficult　to　build　the　model　of　controlled　object　and　design　the　on-line　controller.　To　solve　the　above　problems,　this　paper　proposes　a　data-driven　modeling　and　self-organizing　control　method　for　MSWI　process.　Firstly,　the　model　of　controlled　object　based　on　multi-input　multi-output　Takagi　Sugeno　fuzzy　neural　network　(MIMO-TSFNN)　is　constructed.　Secondly,　a　multi-task　learning　self-organizing　fuzzy　neural　network　controller　(MTL-SOFNNC)　is　designed　to　synchronously　control　the　furnace　temperature　and　flue　gas　oxygen　content,　which　can　self-organize　the　structural　parameters　of　the　controller　by　calculating　the　similarity　of　neurons　and　the　ability　of　multi-task　learning　(MTL).　Meanwhile,　the　stability　of　MTL-SOFNNC　is　proved　by　Lyapunov　theorem.　Finally,　the　effectiveness　of　the　model　and　controller　is　verified　by　the　process　data　of　an　MSWI　plant　in　Beijing.　©　2023　Science　Press.　All　rights　reserved.