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First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.<\/jats:p>","DOI":"10.1177\/0142331220921259","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T07:07:03Z","timestamp":1592291223000},"page":"2833-2856","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems"],"prefix":"10.1177","volume":"42","author":[{"given":"Ahmed","family":"Elkenawy","sequence":"first","affiliation":[{"name":"Department of Industrial Electronics and Control Engineering, Menoufia University, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8092-3387","authenticated-orcid":false,"given":"Ahmad M","family":"El-Nagar","sequence":"additional","affiliation":[{"name":"Department of Industrial Electronics and Control Engineering, Menoufia University, Egypt"}]},{"given":"Mohammad","family":"El-Bardini","sequence":"additional","affiliation":[{"name":"Department of Industrial Electronics and Control Engineering, Menoufia University, Egypt"}]},{"given":"Nabila M","family":"El-Rabaie","sequence":"additional","affiliation":[{"name":"Department of Industrial Electronics and Control Engineering, Menoufia University, Egypt"}]}],"member":"179","published-online":{"date-parts":[[2020,6,16]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.863458"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.122769"},{"issue":"1","key":"e_1_3_3_4_1","first-page":"113","article-title":"Dynamic observers \u2013 a neural net approach","volume":"9","author":"Ahmed MS","year":"2000","unstructured":"Ahmed MS, Riyaz SH (2000) Dynamic observers \u2013 a neural net approach. 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