{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:40:26Z","timestamp":1774964426453,"version":"3.50.1"},"reference-count":46,"publisher":"Informa UK Limited","issue":"7","funder":[{"name":"National Science Foundation","award":["#1912757"],"award-info":[{"award-number":["#1912757"]}]},{"DOI":"10.13039\/501100001659","name":"the Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["#419290163"],"award-info":[{"award-number":["#419290163"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Control"],"published-print":{"date-parts":[[2024,7,2]]},"DOI":"10.1080\/00207179.2023.2212814","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T04:14:34Z","timestamp":1684210474000},"page":"1512-1531","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":10,"title":["A learning- and scenario-based MPC design for nonlinear systems in LPV framework with safety and stability guarantees"],"prefix":"10.1080","volume":"97","author":[{"given":"Yajie","family":"Bao","sequence":"first","affiliation":[{"name":"The University of Georgia","place":["Athens, USA"]}]},{"given":"Hossam S.","family":"Abbas","sequence":"additional","affiliation":[{"name":"University of L\u00fcbeck","place":["L\u00fcbeck, Germany"]}]},{"given":"Javad","family":"Mohammadpour Velni","sequence":"additional","affiliation":[{"name":"Clemson University","place":["Clemson, USA"]}]}],"member":"301","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/rnc.v31.18"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2013.02.003"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.23919\/ACC53348.2022.9867798"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/rnc.v33.5"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207179.2022.2117083"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1115\/DSCC2020-3210"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.7782633"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2020.12.1209"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773"},{"key":"e_1_3_2_11_1","unstructured":"Blundell C. Cornebise J. Kavukcuoglu K. & Wierstra D. (2015 June). Weight uncertainty in neural network. In International conference on machine learning (pp. 1613\u20131622). PMLR."},{"key":"e_1_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC42340.2020.9304310"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2013.02.060"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/(ISSN)1099-1239"},{"key":"e_1_3_2_15_1","unstructured":"Clevert D. A. Unterthiner T. & Hochreiter S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289."},{"key":"e_1_3_2_16_1","unstructured":"Cox P. B. (2018). Towards efficient identification of linear parameter-varying state-space models [Unpublished doctoral dissertation]. Eindhoven University of Technology."},{"key":"e_1_3_2_17_1","unstructured":"Defourny B. (2010). Machine learning solution methods for multistage stochastic programming [PhD diss.]. University of Liege. https:\/\/www.lehigh.edu\/defourny\/PhDthesis_B_Defourny.pdf."},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41108-8"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/9.83532"},{"key":"e_1_3_2_20_1","unstructured":"Gowal S. Dvijotham K. Stanforth R. Bunel R. Qin C. Uesato J. Arandjelovic R. Mann T. & Kohli P. (2018). On the effectiveness of interval bound propagation for training verifiably robust models. arXiv preprint arXiv:1810.12715."},{"key":"e_1_3_2_21_1","unstructured":"Hanema J. (2018). Anticipative model predictive control for linear parameter-varying systems [Unpublished doctoral dissertation]. Technische Universiteit Eindhoven Eindhoven The Netherlands."},{"key":"e_1_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2019.108622"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1049\/cth2.v15.10"},{"key":"e_1_3_2_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5_7"},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.1146\/control.2020.3.issue-1"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021853807313"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.47.2.295.9834"},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/07408170591008082"},{"key":"e_1_3_2_29_1","unstructured":"Koller T. Berkenkamp F. Turchetta M. Boedecker J. & Krause A. (2019). Learning-based model predictive control for safe exploration and reinforcement learning.\u00a0arXiv preprint arXiv: 1906.12189."},{"key":"e_1_3_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2018.8619572"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(96)00255-5"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.5962385"},{"key":"e_1_3_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jprocont.2013.08.008"},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3064680"},{"key":"e_1_3_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2015.08.156"},{"key":"e_1_3_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0005-1098(99)00214-9"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2017.11.001"},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.arcontrol.2020.04.016"},{"key":"e_1_3_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-02827-9_2"},{"key":"e_1_3_2_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2017.08.1512"},{"key":"e_1_3_2_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2017.11.004"},{"key":"e_1_3_2_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0927-0507(03)10006-0"},{"key":"e_1_3_2_44_1","unstructured":"Wicker M. Laurenti L. Patane A. & Kwiatkowska M. (2020). Probabilistic safety for Bayesian neural networks. arXiv preprint arXiv:2004.10281."},{"key":"e_1_3_2_45_1","doi-asserted-by":"crossref","unstructured":"Wills A. & Ninness B. (2012). System identification of linear parameter varying state-space models. In P. Lopes dos Santos \u00a0T. P. Azevedo Perdicoulis &\u00a0C. Novara (Eds.) \u00a0Linear parameter-varying system identification: New developments and trends (pp. 295\u2013315). World Scientific.","DOI":"10.1142\/9789814355452_0011"},{"key":"e_1_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2012.05.020"},{"key":"e_1_3_2_47_1","article-title":"Efficient neural network robustness certification with general activation functions","volume":"31","author":"Zhang H.","year":"2018","unstructured":"Zhang, H., Weng, T. W., Chen, P. Y., Hsieh, C. J., & Daniel, L. (2018). Efficient neural network robustness certification with general activation functions. Advances in Neural Information Processing Systems, 31.","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["International Journal of Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/00207179.2023.2212814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:53:41Z","timestamp":1768928021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/00207179.2023.2212814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":46,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7,2]]}},"alternative-id":["10.1080\/00207179.2023.2212814"],"URL":"https:\/\/doi.org\/10.1080\/00207179.2023.2212814","relation":{},"ISSN":["0020-7179","1366-5820"],"issn-type":[{"value":"0020-7179","type":"print"},{"value":"1366-5820","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,22]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tcon20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=tcon20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2022-07-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}