[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118994-en":3,"doc-seo-118994-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118994,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Risk-aware and Robust Approaches for Machine Learning-supported Model Predictive Control for Iterative Processes - Doctoral Thesis","This doctoral thesis investigates risk-aware and robust control strategies for model predictive control (MPC) in iterative processes using machine learning. It addresses the challenge that learned models may become inaccurate when connections to underlying physical laws are only partially preserved. The work proposes two data-driven uncertainty modeling methods: one combining Gaussian processes for uncertainty and neural networks for the nominal model, and another using tube-based MPC with a “safe set” concept to guarantee constraint satisfaction under stated assumptions.","| Risk-aware and Robust Approaches for Machine Learning-supported Model Predictive Control for Iterative Processes |\n| --- |\n| Zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.) Genehmigte Dissertation von Bruno Morabito aus Reggio Calabria Tag der Einreichung: 27.07.2023, Tag der Prüfung: 20.12.2023\u003Cbr>1. Gutachten: Prof. Dr.-Ing. Rolf Findeisen\u003Cbr>2. Gutachten: Prof. Dr.-Ing. Sergio Lucia\u003Cbr>Darmstadt, Technische Universität Darmstadt, 2023 |\n| \u003Cbr>Electrical Engineering and Information Technology Department\u003Cbr>Institute of Automatic Control and Mechatronics\u003Cbr>Control and Cyber-Physical Systems |\n\nRisk-aware and Robust Approaches for Machine Learning-supported Model Predictive Control for Iterative Processes  \nAccepted doctoral thesis by Bruno Morabito  \nDate of submission: 27.07.2023  \nDate of thesis defense: 20.12.2023  \nDarmstadt, Technische Universität Darmstadt, 2023  \nBitte zitieren Sie dieses Dokument als: URN: urn:nbn:de:tuda-tuprints-264993  \nURL: [http://tuprints.ulb.tu-darmstadt.de/26499](http://tuprints.ulb.tu-darmstadt.de/26499)  \n[Jahr der Ver](Jahr der Ver)ö[ffentlichung auf TUprints: 2024](ffentlichung auf TUprints: 2024)  \nDieses Dokument wird bereitgestellt von tuprints, E-Publishing-Service der TU Darmstadt [http://tuprints.ulb.tu-darmstadt.de](http://tuprints.ulb.tu-darmstadt.de)[ ](http://tuprints.ulb.tu-darmstadt.de)[tuprints@ulb.tu-darmstadt.de](tuprints@ulb.tu-darmstadt.de)  \nDie Veröffentlichung steht unter folgender Creative Commons Lizenz: Namensnennung – Weitergabe unter gleichen Bedingungen 4.0 International [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)  \nThis work is licensed under a Creative Commons License: Attribution–ShareAlike 4.0 International [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)  \nTo my aunt Brunella  \nErklärungen laut Promotionsordnung  \n§ 8 Abs. 1 lit. c PromO  \nIch versichere hiermit, dass die elektronische Version meiner Dissertation mit der schriftlichen Version übereinstimmt.  \n§ 8 Abs. 1 lit. d PromO  \nIch versichere hiermit, dass zu einem vorherigen Zeitpunkt noch keine Promotion versucht wurde. In diesem Fall sind nähere Angaben über Zeitpunkt, Hochschule, Dissertationsthema und Ergebnis dieses Versuchs mitzuteilen.  \n§ 9 Abs. 1 PromO  \nIch versichere hiermit, dass die vorliegende Dissertation selbstständig und nur unter Verwendung der angegebenen Quellen verfasst wurde.  \n§ 9 Abs. 2 PromO  \nDie Arbeit hat bisher noch nicht zu Prüfungszwecken gedient.  \nDarmstadt, 27 .07.2023    \nBruno Morabito  \nv  \nAcknowledgements  \nAs I present this Ph.D. thesis, I am reminded of the numerous individuals who have contributed to this journey, each playing a vital role.  \nFirst off, I would like to express my sincere appreciation to Prof. Rolf Findensen for granting me the opportunity to embark on this project.  \nI am very gratefull to all my colleagues, especially to my closest fellow academic travelers: Johannes Pohlodek, Sebastián Espinel Ríos, Rudolph Kok, Hoang Hai Nguyen, Petar Andonov, Mohammed Soliman and Mohammed Ibrahim. We shared more than just research ideas and deadlines. From brainstorming sessions and arabic language classes, you made sure this journey was anything but dull. Our combined intellectual horsepower made for some memorable meetings and even more memorable pub gatherings.  \nI am also grateful to the international Max Planck Research School for its supportive and dynamic academic environment.  \nOn a personal note, my deepest gratitude goes to my family, whose unwavering support and encouragement have been a constant source of strength. To my girlfriend, Izel Avcı, Iowe special thanks for her understanding and unwavering support.  \nAnd to my friends, particularly the Forró community of Magdeburg, who proved that Ph.D. students can indeed dance – thank you for the much-needed breaks.  \nIn conclusion, this thesis is a culmination of not just my","cbCailhqnapkuGYT","https://ap.wps.com/l/cbCailhqnapkuGYT","pdf",38562450,1,178,"English","en",105,"# Acknowledgements\n# Zusammenfassung\n## Motivation\n## Proposed methods","[{\"question\":\"Why does the thesis focus on risk-aware control for machine learning-supported MPC?\",\"answer\":\"Machine learning can simplify modeling, but it may produce highly inaccurate results when physical law connections are lost, so uncertainty must be explicitly accounted for in the control design.\"},{\"question\":\"What is the first proposed uncertainty modeling method?\",\"answer\":\"It uses Gaussian processes to learn model uncertainty and neural networks to learn the nominal model, summarizing uncertainty into a single parameter for a risk-aware MPC decision process.\"},{\"question\":\"How does the second method guarantee constraint satisfaction?\",\"answer\":\"It is based on tube-based MPC and the concept of a “safe set,” where a feasible solution exists, and it shows that the safe set can expand under certain assumptions across process iterations.\"}]","Risk-aware and Robust Approaches for Machine Learning-supported Model Predictive Control for Iterative Processes - Doctoral Thesis | PDF",1785721526,449,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"risk-aware-and-robust-approaches-for-machine-learning-supported-model-predictive-control-for-iterative-processes-doctoral-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/risk-aware-and-robust-approaches-for-machine-learning-supported-model-predictive-control-for-iterative-processes-doctoral-thesis/118994/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does the thesis focus on risk-aware control for machine learning-supported MPC?","Question",{"text":75,"@type":76},"Machine learning can simplify modeling, but it may produce highly inaccurate results when physical law connections are lost, so uncertainty must be explicitly accounted for in the control design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the first proposed uncertainty modeling method?",{"text":80,"@type":76},"It uses Gaussian processes to learn model uncertainty and neural networks to learn the nominal model, summarizing uncertainty into a single parameter for a risk-aware MPC decision process.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the second method guarantee constraint satisfaction?",{"text":84,"@type":76},"It is based on tube-based MPC and the concept of a “safe set,” where a feasible solution exists, and it shows that the safe set can expand under certain assumptions across process iterations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]