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To guarantee that state constraints always remain in the asymmetric time\u2010varying constraint regions, the asymmetric time\u2010varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time\u2010varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with time\u2010varying constraints. Using Lyapunov analysis, all signals in the closed\u2010loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.<\/jats:p>","DOI":"10.1155\/2018\/5082401","type":"journal-article","created":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T23:33:43Z","timestamp":1517441623000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time\u2010Varying State Constraints"],"prefix":"10.1155","volume":"2018","author":[{"given":"Lei","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1584-8528","authenticated-orcid":false,"given":"Dapeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,1,31]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2015.2487523"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2017.2652394"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2016.7373768"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2017.7510544"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2651059"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2012.2213092"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tase.2016.2517155"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2015.2398833"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/rnc.1609"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-cta.2009.0428"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2017.2738155"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2311824"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2038999"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2411671"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2015.2420543"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2017.2774185"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2329931"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.12.010"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2015.2426131"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.09.406"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2016.2612646"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2004.826130"},{"key":"e_1_2_8_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/tfuzz.2014.2327987"},{"key":"e_1_2_8_24_2","article-title":"Adaptive neural tracking control for an uncertain state constrained robotic manipulator with time-varying delays","author":"Li D. 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