{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:05:52Z","timestamp":1778047552646,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T00:00:00Z","timestamp":1559606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Research Funds for Central Universities","award":["NF2018003"],"award-info":[{"award-number":["NF2018003"]}]},{"name":"Q. Lan and the 333 Project"},{"name":"Six Talents Peak Project of Jiangsu Province"},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["51576096"],"award-info":[{"award-number":["51576096"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:p>A novel nonlinear model predictive control method for aero-engine direct thrust control is proposed to improve engine response ability and reduce computational complexity of nonlinear model predictive control. The control objective of the proposed method is the thrust directly instead of the measurable parameters. The linearized model based on online sliding window deep neural network is proposed as predictive model. The online sliding window deep neural network has strong fitting capacity for nonlinear object and adopted to fitting the transient process of engine. The back propagation is adopted to obtain linearized model of online sliding window deep neural network, which greatly reduce the calculated amount. The comparison simulations of the popular nonlinear model predictive control based on extended Kalman filter and the proposed one are carried out. The simulation results show that compared with the popular nonlinear model predictive control, the proposed nonlinear model predictive control not only has the better response ability but also has reduced computational complexity greatly, nearly reduce computation time more than 35 ms.<\/jats:p>","DOI":"10.1177\/0959651819853395","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T05:39:49Z","timestamp":1559713189000},"page":"330-337","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor"],"prefix":"10.1177","volume":"234","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8055-5633","authenticated-orcid":false,"given":"Qiangang","family":"Zheng","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing, China"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing, China"}]},{"given":"Fengyong","family":"Sun","sequence":"additional","affiliation":[{"name":"AECC Aero Engine Control System Institute, Wuxi, China"}]},{"given":"Haibo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Jiangsu Province Key Laboratory of Aerospace Power System, Nanjing, China"}]}],"member":"179","published-online":{"date-parts":[[2019,6,4]]},"reference":[{"key":"bibr1-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1177\/0959651816633352"},{"key":"bibr2-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1177\/095965180421800301"},{"key":"bibr3-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1177\/0954410016683412"},{"key":"bibr4-0959651819853395","unstructured":"Sun JG, Vasilyev V, Ilyasov B. Advanced multivariable control systems of aero-engines. Beijing, China: Beijing University of Aeronautics & Astronautics, 2005."},{"key":"bibr5-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1002\/rnc.3673"},{"key":"bibr6-0959651819853395","doi-asserted-by":"publisher","DOI":"10.2514\/1.G001802"},{"key":"bibr7-0959651819853395","doi-asserted-by":"publisher","DOI":"10.2514\/1.13048"},{"key":"bibr8-0959651819853395","first-page":"4002","volume-title":"49th AIAA\/ASME\/SAE\/ASEE joint propulsion conference","author":"Connolly JW"},{"key":"bibr9-0959651819853395","volume-title":"51st AIAA\/SAE\/ASEE joint propulsion conference","author":"Csank JT"},{"key":"bibr10-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1115\/1.2818518"},{"key":"bibr11-0959651819853395","unstructured":"Van Essen HA, De Lange HC. Nonlinear model predictive control experiments on a laboratory gas turbine installation. In: ASME turbo expo 2000: power for land, sea, and air, Munich, 8\u201311 May 2000, p. V004T04A012. New York: American Society of Mechanical Engineers."},{"key":"bibr12-0959651819853395","first-page":"4649","volume-title":"Proceedings of the 41st IEEE conference on decision and control","volume":"4","author":"Brunell BJ"},{"key":"bibr13-0959651819853395","doi-asserted-by":"publisher","DOI":"10.2514\/1.25846"},{"key":"bibr14-0959651819853395","volume-title":"Introduction to advanced engine control concepts","author":"Garg S","year":"2007"},{"key":"bibr15-0959651819853395","doi-asserted-by":"publisher","DOI":"10.2514\/1.30591"},{"issue":"1","key":"bibr16-0959651819853395","first-page":"84","volume":"20","author":"Di Cairano S","year":"2012","journal-title":"IEEE T Contr Syst T"},{"key":"bibr17-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2014.2309671"},{"key":"bibr18-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1515\/tjj-2018-0004"},{"key":"bibr19-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2018.01.034"},{"key":"bibr20-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1515\/tjj-2018-0049"},{"key":"bibr21-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2789935"},{"key":"bibr22-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1177\/0959651817710127"},{"key":"bibr23-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"bibr24-0959651819853395","unstructured":"Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge, MA: MIT Press, 2016."},{"issue":"1","key":"bibr25-0959651819853395","first-page":"214","volume":"21","author":"Liu W","year":"2016","journal-title":"IEEE\/ASME T Mech"},{"key":"bibr26-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(01)00143-1"},{"key":"bibr27-0959651819853395","doi-asserted-by":"publisher","DOI":"10.1007\/s12532-014-0071-1"},{"key":"bibr28-0959651819853395","first-page":"324181","volume":"2014","author":"Liu CS","year":"2014","journal-title":"J Appl Math"}],"container-title":["Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0959651819853395","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0959651819853395","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0959651819853395","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:38:18Z","timestamp":1777675098000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0959651819853395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,4]]},"references-count":28,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["10.1177\/0959651819853395"],"URL":"https:\/\/doi.org\/10.1177\/0959651819853395","relation":{},"ISSN":["0959-6518","2041-3041"],"issn-type":[{"value":"0959-6518","type":"print"},{"value":"2041-3041","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,4]]}}}