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A composite robust adaptive self\u2010structuring neural\u2010network\u2010bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line\u2010of\u2010sight range and angle can be obtained. An adaptive self\u2010structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input\u2010to\u2010state stability of the total closed\u2010loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1155\/2021\/2010493","type":"journal-article","created":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T03:35:18Z","timestamp":1640144118000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Robust Adaptive Self\u2010Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface 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