Accurate　positioning　of　the　flexible　endoscope　during　clinical　procedures　is　critical　for　the　visualization　and　success　of　surgical　interventions.　However,　manual　operation　of　the　endoscope　is　challenging,　especially　for　less-experienced　users,　due　to　visual　reasoning　and　hand-eye　coordination　requirements.　To　achieve　automatic　steering　of　the　endoscope,　this　work　proposes　a　closed-loop　robust　control　method　that　allows　the　endoscope　to　smoothly　travel　along　the　upper　respiratory　tract.　First,　an　attention　neural　network　is　introduced　to　segment　the　lumen　region　in　endoscopic　images,　which　indicates　the　moving　direction　of　the　endoscope.　The　segmentation　results　are　optimized　by　convolutional　conditional　random　field　(ConvCRF).　In　addition,　to　steer　the　flexible　endoscope,　an　active　disturbance　rejection　control　(ADRC)　strategy　is　proposed　to　adjust　the　orientation　of　the　endoscope　tip.　An　external　state　observer　(ESO)　is　designed　to　estimate　environmental　disturbances,　enabling　the　controller　to　compensate　for　the　positioning　error.　Finally,　the　segmentation　method　achieved　the　mIOU,　accuracy,　and　Dice　values　of　85.4%,　97.5%,　and　91.4%,　respectively,　outperforming　state-of-the-art　methods.　The　performance　of　the　controller　was　validated　on　a　real　robot　showing　the　ability　to　automatically　steer　the　flexible　endoscope　along　the　upper　respiratory　tract.