Affected　by　high　temperature　and　serious　pollution,　some　key　parameters　in　incinerator　are　difficult　to　obtain　directly,　and　the　detectable　data　also　contain　outliers　and　noise,　which　makes　it　extremely　difficult　to　mine　the　relationship　between　variables　and　derive　control　rules.　To　solve　these　problems,　an　event-triggered　online　learning　fuzzy-neural　robust　controller　is　proposed　and　applied　to　the　furnace　temperature　control　of　municipal　solid　waste　incineration　(MSWI)　process.　First,　the　outliers　are　eliminated　by　the　box-plot　method,　and　the　data　is　denoised　with　a　Gaussian　filter.　Second,　the　controlled　object　model　based　on　T-S　fuzzy　neural　network　is　constructed　by　data-driven　method.　Third,　an　online　learning　fuzzy　neural　controller　is　designed　to　be　imposed　on　the　established　controlled　object　model,　which　adaptively　adjusts　the　network　structure　by　calculating　the　information　transmission　strength　of　the　rule　neurons.　Meanwhile,　an　event-triggered　mechanism　is　introduced　to　reduce　actuator　wear　and　save　energy　while　maintaining　control　effectiveness.　Then,　the　convergence　of　the　controller　is　deduced　by　Lyapunov＇s　second　law.　Finally,　the　validity　of　the　proposed　method　is　verified　by　process　data　from　a　real　MSWI　plant　in　Beijing,　China.　The　results　show　that　the　method　proposed　in　this　paper　can　build　the　furnace　temperature　model　from　imperfect　data,　and　the　controller　can　grow　and　prune　neurons　autonomously,　which　improves　the　control　accuracy　and　efficiency　of　the　furnace　temperature　under　external　disturbances.