[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118192-en":3,"doc-seo-118192-105":30,"detail-sidebar-cat-0-en-105":83},{"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},118192,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Statistical Machine Learning-Based Predictive Control of Nonlinear Processes - Doctor of Philosophy","Data-driven modeling underpins industrial process control, yet classical linear approaches struggle with large-scale, highly nonlinear dynamics common in process engineering. This dissertation integrates machine learning with model predictive control to stabilize nonlinear chemical processes where time delays and two-time-scale behavior degrade performance and can trigger instability or oscillations. It develops Lyapunov-based MPC for delayed systems and a learning-based predictor to compensate input delays, and derives generalization error bounds for recurrent neural networks, partially connected RNNs, and LSTM models.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nStatistical Machine Learning-Based Predictive Control of Nonlinear Processes  \nPermalink  \n[https://escholarship.org/uc/item/2fd5v7tn](https://escholarship.org/uc/item/2fd5v7tn)  \nAuthor  \nAlnajdi, Aisha  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nStatistical Machine Learning-Based Predictive Control of Nonlinear Processes  \nA dissertation submitted in partial satisfaction of the requirement for the degree Doctor of Philosophy in Electrical and Computer Engineering  \nby  \nAisha M A S Alnajdi  \n2024  \n© Copyright by Aisha M A S Alnajdi  \n2024  \nABSTRACT OF THE DISSERTATION  \nStatistical Machine Learning-Based Predictive Control of Nonlinear Processes  \nby  \nAisha M A S Alnajdi  \nDoctor of Philosophy in Electrical and Computer Engineering  \nUniversity of California, Los Angeles, 2024  \nProfessor Panagiotis D. Christofides, Chair  \nData are an essential factor in the fourth industrial revolution, demanding engineers and scientists to leverage and analyze their potential for significantly improving the efficiency of industrial processes and their control systems. In classical industrial process control systems, the models are constructed using linear data-driven approaches, where parameters are adjusted based on experimental or simulated data. In certain critical control loops focused on optimizing profits, first-principles models are used to describe the fundamental physicochemical phenomena, incorporating a small set of parameters derived from industrial or simulation data. However, despite the effectiveness of these classical modeling methods in many studies, there persists a significant challenge when modeling large-scale, complex non-  \nlinear systems within the field of process engineering. Traditional approaches often fall short of accurately representing the complexities and nonlinear dynamics inherent in large-scale industrial processes. Therefore, there are continuous efforts to conduct extensive studies on effective tools for model development and evaluation techniques. This is crucial because process models play a central role in advanced control strategies, particularly, model-based control systems such as model predictive control (MPC) and economic MPC (EMPC) frameworks. Therefore, accurate construction and evaluation of these models will contribute to achieving the desired performance and ensuring operational efficiency, ultimately leading to robust and reliable control systems.  \nMachine learning techniques have proven to be an effective modeling tool in many engineering applications. More specifically, machine learning models have been used to model large-scale, complex nonlinear systems. These models are then integrated into MPC to achieve closed-loop stability. Among the many types of machine learning techniques, recurrent neural networks (RNNs) are widely used to model nonlinear processes involving time series data. This is due to their special structure, which allows useful previous information to be retained.  \nIn addition to complexities arising from nonlinearities and the large-scale nature of practical industrial processes, and challenges in modeling these systems, time delays pose significant challenges in nonlinear control systems. These delays can arise due to various sources such as transportation lags, sensor and actuator response times. Such delays can lead to instability, oscillations, and overall degradation in the performance of the control system. Hence, addressing these delays is crucial for maintaining the system’s stability  \nand optimizing its performance. Besides time-delay systems, there are also systems that experience different time-scale multiplicity, known as two-time scale systems. 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