Municipal　solid　waste　incineration　(MSWI)　is　an　industrial　process　with　multiple　mechanism　reactions,　which　has　strong　coupling　and　time-varying　dynamics.　It　is　extremely　difficult　to　design　a　feasible　multivariable　controller　for　MSWI　process　due　to　the　complex　composition　of　municipal　solid　waste　and　fluctuation　of　calorific　value.　To　solve　these　problems,　a　cooperative　event-triggered　fuzzy-neural　multivariable　controller　with　multi-task　learning　(CETFNMC-MTL)　is　proposed　to　realize　the　adaptive　multivariable　control　of　MSWI　process.　First,　a　fuzzy-neural　multivariable　controller　is　established　to　control　furnace　temperature　and　oxygen　content　synchronously.　Second,　a　dynamic　self-organizing　mechanism　based　on　multi-task　learning　is　designed,　which　splits　and　merges　neurons　by　calculating　the　dynamic　time　warping　distance　and　cumulative　contribution　of　neurons　in　the　continuous　time.　Third,　a　cooperative　event-triggered　mechanism　is　introduced　to　improve　controller　update　efficiency　while　reducing　mechanical　wear　and　computational　burden.　Then,　the　stability　of　parameters　learning　and　structure　self-organizing　process　is　analyzed　to　guarantee　the　successful　application　of　CETFNMC-MTL.　Finally,　the　effectiveness　of　the　controller　is　tested　with　process　data　from　an　MSWI　plant　in　Beijing,　China.　The　results　show　that　the　proposed　CETFNMC-MTL　has　adaptive　learning　ability,　while　reducing　energy　consumption　and　improving　control　accuracy.　IEEE