{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:31:15Z","timestamp":1764174675889,"version":"3.37.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030205171"},{"type":"electronic","value":"9783030205188"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20518-8_43","type":"book-chapter","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:02:40Z","timestamp":1559674960000},"page":"515-526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Neural Network-Based Approach to Sensor and Actuator Fault-Tolerant Control"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2469-7570","authenticated-orcid":false,"given":"Marcin","family":"Pazera","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3078-411X","authenticated-orcid":false,"given":"Marcin","family":"Mrugalski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0031-0004","authenticated-orcid":false,"given":"Marcin","family":"Witczak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0801-0145","authenticated-orcid":false,"given":"Mariusz","family":"Buciakowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"issue":"3","key":"43_CR1","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.automatica.2005.10.013","volume":"42","author":"A Alessandri","year":"2006","unstructured":"Alessandri, A., Baglietto, M., Battistelli, G.: Design of state estimators for uncertain linear systems using quadratic boundedness. Automatica 42(3), 497\u2013502 (2006)","journal-title":"Automatica"},{"issue":"1","key":"43_CR2","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2478\/amcs-2019-0008","volume":"29","author":"J Cayero","year":"2019","unstructured":"Cayero, J., Rotondo, D., Morcego, B., Puig, V.: Optimal state observation using quadratic boundedness: application to UAV disturbance estimation. Int. J. Appl. Math. Comput. Sci. 29(1), 99\u2013109 (2019)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"43_CR3","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.conengprac.2015.12.006","volume":"49","author":"L Chen","year":"2016","unstructured":"Chen, L., Patton, R., Goupil, P.: Robust fault estimation using an LPV reference model: addsafe benchmark case study. Control Eng. Pract. 49, 194\u2013203 (2016)","journal-title":"Control Eng. Pract."},{"issue":"4","key":"43_CR4","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/S0167-6911(99)00035-3","volume":"37","author":"MC Oliveira de","year":"1999","unstructured":"de Oliveira, M.C., Bernussou, J., Geromel, J.C.: A new discrete-time robust stability condition. Syst. Control Lett. 37(4), 261\u2013265 (1999)","journal-title":"Syst. Control Lett."},{"issue":"5","key":"43_CR5","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1109\/TFUZZ.2011.2147320","volume":"19","author":"B Ding","year":"2011","unstructured":"Ding, B.: Dynamic output feedback predictive control for nonlinear systems represented by a Takagi-Sugeno model. IEEE Trans. Fuzzy Syst. 19(5), 831\u2013843 (2011)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"8","key":"43_CR6","doi-asserted-by":"publisher","first-page":"3485","DOI":"10.1109\/TIE.2013.2244537","volume":"60","author":"GHB Foo","year":"2013","unstructured":"Foo, G.H.B., Zhang, X., Vilathgamuwa, D.M.: A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter. IEEE Trans. Ind. Electron. 60(8), 3485\u20133495 (2013)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"1","key":"43_CR7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.automatica.2006.08.002","volume":"43","author":"S Gillijns","year":"2007","unstructured":"Gillijns, S., De Moor, B.: Unbiased minimum-variance input and state estimation for linear discrete-time systems. Automatica 43(1), 111\u2013116 (2007)","journal-title":"Automatica"},{"key":"43_CR8","volume-title":"Neural Networks and Learning Machines","author":"S Haykin","year":"2009","unstructured":"Haykin, S.: Neural Networks and Learning Machines, vol. 3. Pearson, Upper Saddle River (2009)"},{"key":"43_CR9","unstructured":"INTECO. Multitank System - User\u2019s manual (2013). \n                      www.inteco.com.pl"},{"issue":"6","key":"43_CR10","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1002\/rnc.3051","volume":"24","author":"HK Khalil","year":"2014","unstructured":"Khalil, H.K., Praly, L.: High-gain observers in nonlinear feedback control. Int. J. Robust Nonlinear Control 24(6), 993\u20131015 (2014)","journal-title":"Int. J. Robust Nonlinear Control"},{"key":"43_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-01547-7","volume-title":"Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis","author":"M Mrugalski","year":"2014","unstructured":"Mrugalski, M.: Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis. Springer, Cham (2014). \n                      https:\/\/doi.org\/10.1007\/978-3-319-01547-7"},{"key":"43_CR12","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.isatra.2016.01.002","volume":"61","author":"M Mrugalski","year":"2016","unstructured":"Mrugalski, M., Luzar, M., Pazera, M., Witczak, M., Aubrun, C.: Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system. ISA Trans. 61, 318\u2013328 (2016)","journal-title":"ISA Trans."},{"key":"43_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-04323-3","volume-title":"Non-linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models","author":"O Nelles","year":"2001","unstructured":"Nelles, O.: Non-linear Systems Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin (2001). \n                      https:\/\/doi.org\/10.1007\/978-3-662-04323-3"},{"key":"43_CR14","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427\u2013436 (2015)","DOI":"10.1109\/CVPR.2015.7298640"},{"issue":"18","key":"43_CR15","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1002\/rnc.1313","volume":"18","author":"EG Nobrega","year":"2008","unstructured":"Nobrega, E.G., Abdalla, M.O., Grigoriadis, K.M.: Robust fault estimation of uncertain systems using an LMI-based approach. Int. J. Robust Nonlinear Control 18(18), 1657\u20131680 (2008)","journal-title":"Int. J. Robust Nonlinear Control"},{"key":"43_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84882-653-3","volume-title":"Fault-tolerant Control Systems: Design and Practical Applications","author":"H Noura","year":"2009","unstructured":"Noura, H., Theilliol, D., Ponsart, J.C., Chamseddine, A.: Fault-tolerant Control Systems: Design and Practical Applications. Springer, London (2009). \n                      https:\/\/doi.org\/10.1007\/978-1-84882-653-3"},{"issue":"2","key":"43_CR17","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2478\/amcs-2018-0021","volume":"28","author":"M Pazera","year":"2018","unstructured":"Pazera, M., Buciakowski, M., Witczak, M.: Robust multiple sensor fault-tolerant control for dynamic non-linear systems: application to the aerodynamical twin-rotor system. Int. J. Appl. Math. Comput. Sci. 28(2), 297\u2013308 (2018)","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"43_CR18","volume-title":"Artificial Intelligence: A Modern Approach","author":"SJ Russell","year":"2016","unstructured":"Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)"},{"issue":"7","key":"43_CR19","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1002\/rnc.3365","volume":"26","author":"M Witczak","year":"2015","unstructured":"Witczak, M., Buciakowski, M., Puig, V., Rotondo, D., Nejjari, F.: An LMI approach to robust fault estimation for a class of nonlinear systems. Int. J. Robust Nonlinear Control 26(7), 1530\u20131548 (2015)","journal-title":"Int. J. Robust Nonlinear Control"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20518-8_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:14:48Z","timestamp":1559675688000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20518-8_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030205171","9783030205188"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20518-8_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"210","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"150","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"71% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}