[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123740-en":3,"doc-seo-123740-105":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},123740,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Feedback controller design and process modeling methods using machine learning","This dissertation presents feedback controller design and process modeling approaches enhanced by machine learning. The work develops methods that connect data-driven models with control structures to improve performance in process systems. It emphasizes modeling strategies suited for tracking and economic model predictive control settings, including proportional-integral controller design. Contributions include practical design workflows, integration of machine learning components, and validation through computational tools commonly used in scientific research.","UC Santa Barbara  \nUC Santa Barbara Electronic Theses and Dissertations  \nTitle  \nFeedback controller design and process modeling methods using machine learning  \nPermalink  \n[https://escholarship.org/uc/item/1qx4m3xn](https://escholarship.org/uc/item/1qx4m3xn)  \nAuthor  \nKumar, Pratyush  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUniversity of California  \nSanta Barbara  \nFeedback controller design and process modeling methods using machine learning  \nA dissertation submitted in partial satisfaction  \nof the requirements for the degree  \nDoctor of Philosophy  \nin  \nChemical Engineering  \nby  \nPratyush Kumar  \nCommittee in charge:  \nProfessor James B. Rawlings, Chair  \nProfessor Michael F. Doherty  \nProfessor Todd M. Squires  \nProfessor João P. Hespanha  \nMarch 2023  \nThe Dissertation of Pratyush Kumar is approved.  \n\n| Professor Michael F. Doherty |\n| --- |\n| Professor Todd M. Squires |\n| Professor João P. Hespanha |\n\nProfessor James B. Rawlings, Committee Chair  \nJanuary 2023  \nFeedback controller design and process modeling methods using machine learning  \nCopyright © 2023  \nby  \nPratyush Kumar  \nTo my family.  \niv  \nAcknowledgments  \nI express my sincere gratitude to my advisor Prof. James Rawlings for his guidance and support throughout my PhD studies. Without his supervision, knowledge, and experience, I would not have been able to accomplish the work in this thesis. I am thankful to him for always challenging me to work on impactful research problems, and also for his patience and encouragement during my graduate school. His eagerness to teach and learn from students has always inspired me. I have grown both personally and professionally during my time in his research group.  \nI thank Profs. Mike Doherty, Todd Squires, and Joao Hespanha for taking the time to serve on my PhD committee. I am also grateful to Prof. Stephen Wright from UW Madison for his advice and collaboration on some machine learning aspects of my research.  \nDuring my PhD studies, I had an opportunity to closely work with a few industrial practitioners. I am thankful to Drs. Robert Turney, Michael Wenzel, Mohammad Elsbat, and Michael Risbeck from Johnson Controls International (JCI) . My time as an intern at JCI was highly useful to gain an industrial perspective on control systems technologies and generate impactful research ideas. It was also a pleasure to work with Dr. Pierre Carrette from Shell on a project about MPC-PI cascade control systems in industrial applications.  \nI also thank my undergraduate Profs. Ravindra Gudi, Mani Bhushan, Sachin Patwardhan, and Vinay Prasad who initially introduced me to the field of process control and optimization.  \nI have been fortunate to work with a few great colleagues in the Rawlings group. In my first year at UW Madison, Michael Risbeck and Nishith Patel provided assistance on group software and were helpful in MPC related questions. I thank Michael additionally for engaging in technical discussions during my internship at JCI, and for creating highly useful group software tools that saved valuable time in research projects. Travis Arnold and Douglas Allan were great colleagues at both UW Madison and UCSB. They were always willing to engage in technical discussions on controls topics and research ideas. Koty McAllister has been an amazing colleague and friend throughout my graduate school. The move from UW Madison to UCSB was smooth due to his support and good humor. I also thank him for the many outside of work fun activities he engaged with me in Madison and Santa Barbara. It has been my pleasure to work with the younger Rawlings group members Steven Kuntz, Chris Kuo-Leblanc, Davide Mannini, and Titus Quah. I wish them all the best in the rest of their PhD studies and future endeavors.  \nMy PhD work would have been very different without access to the powerful scientific computing s","cbCaitHVDaLDY54P","https://ap.wps.com/l/cbCaitHVDaLDY54P","pdf",3296041,1,245,"English","en",105,"# Acknowledgments\n## Doctor of Philosophy committee and approvals\n## Professional guidance and collaborations\n## Industrial and academic experiences\n## Computing tools and software acknowledgments","[{\"question\":\"What is the main topic of the dissertation?\",\"answer\":\"The dissertation focuses on feedback controller design and process modeling methods that use machine learning.\"},{\"question\":\"How does the work relate to model predictive control?\",\"answer\":\"It addresses modeling and control formulations connected to tracking and economic model predictive control, including proportional-integral controller modeling.\"},{\"question\":\"Which tools and software are mentioned as important for the research?\",\"answer\":\"The acknowledgments mention CasADi, TensorFlow, MPCTools, and Matplotlib as valuable software used during the PhD work.\"}]","Feedback controller design and process modeling methods using machine learning | 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