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This article presents a novel adaptive neural network controller with extended state observer for the human\u2013robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human\u2013machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human\u2013machine interactions to enhance productivity and efficiency.<\/jats:p>","DOI":"10.1177\/0959651819898936","type":"journal-article","created":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T06:45:02Z","timestamp":1580971502000},"page":"820-833","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive neural impedance control with extended state observer for human\u2013robot interactions by output feedback through tracking differentiator"],"prefix":"10.1177","volume":"234","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7350-6702","authenticated-orcid":false,"given":"JianTao","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, 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