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However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system\u2019s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the \u201cexplosion of complexity\u201d issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder. <\/jats:p>","DOI":"10.1177\/09596518241246825","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T06:52:21Z","timestamp":1716533541000},"page":"1535-1555","update-policy":"http:\/\/dx.doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer"],"prefix":"10.1177","volume":"238","author":[{"given":"Liyan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China"},{"name":"State Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology, Xuzhou, 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