{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:16Z","timestamp":1776274516749,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T00:00:00Z","timestamp":1613952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a radial basis neural network adaptive sliding mode controller (RBF\u2212NN ASMC) for nonlinear electromechanical actuator systems is proposed. The radial basis function neural network (RBF\u2212NN) control algorithm is used to compensate for the friction disturbance torque in the electromechanical actuator system. An adaptive law was used to adjust the weights of the neural network to achieve real\u2212time compensation of friction. The sliding mode controller is designed to suppress the model uncertainty and external disturbance effects of the electromechanical actuator system. The stability of the RBF\u2212NN ASMC is analyzed by Lyapunov\u2019s stability theory, and the effectiveness of this method is verified by simulation. The results show that the control strategy not only has a better compensation effect on friction but also has better anti\u2212interference ability, which makes the electromechanical actuator system have better steady\u2212state and dynamic performance.<\/jats:p>","DOI":"10.3390\/s21041508","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T20:42:51Z","timestamp":1614026571000},"page":"1508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Friction Compensation Control of Electromechanical Actuator Based on Neural Network Adaptive Sliding Mode"],"prefix":"10.3390","volume":"21","author":[{"given":"Wei","family":"Ruan","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Quanlin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Xiaoyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8154-600X","authenticated-orcid":false,"given":"Zhibing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, C., Gan, M., and Qiao, Z. 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