[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119384-en":3,"doc-seo-119384-105":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":20,"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},119384,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC","Model-based optimization methods for monitoring and control, including model predictive control and optimal state/parameter estimation, rely on accurate models for dynamics, constraints, and performance criteria. With advances in digitalization, deep learning, and computing, machine-learning-based components increasingly complement model-based approaches, yet practical open-source tools remain limited. This article presents an easy-to-use Python toolbox that rapidly formulates and solves machine-learning-supported optimization, MPC, and estimation problems, leveraging modern ML libraries to train problem components. HILO-MPC supports tasks from stabilization and tracking to moving-horizon estimation and Kalman filtering, enables fast embedded MPC code generation for linear systems, and includes examples for research and teaching.","DOI: 10.1002/rnc.7275  \nRESEARCH ARTICLE   \nFlexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC  \nJohannes Pohlodek1  Bruno Morabito2  Christian Schlauch3  Pablo Zometa4  Rolf Findeisen1  \n1 Control and Cyber-Physical Systems Laboratory, TU Darmstadt, Darmstadt, Germany  \n2 R&D Department, Yokogawa Insilico Biotechnology GmbH, Stuttgart, Germany  \n3 Research Center Trustworthy Data Science and Security, TU Dortmund, Darmstadt, Germany  \n4 Faculty of Engineering, German International University, Berlin, Germany  \nCorrespondence  \nRolf Findeisen, Control and Cyber-Physical Systems Laboratory, TU Darmstadt, Germany.  \nEmail: rolf.findeisen@tu-darmstadt.de  \nFunding information  \nDIGIPOL/EU-EFRE Sachsen-Anhalt  \nAbstract  \nModel-based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model-based approaches. Thanks to recent technological advancements in digitalization, machine-learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model-based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model-based control and optimization is increasing rapidly. However, there are no easy-to-use, flexible, and freely available open-source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy-to-use Python toolbox that allows to solve machine-learning-supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state-of-the-art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO-MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.  \nKEYW O RDS  \nestimation, model-based optimal estimation and control, machine learning, model predictive control, python, open-source toolbox, optimization  \n\n| Johannes Pohlodek and Bruno Morabito contributed equally to this study. |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.\u003Cbr>© 2024 The Authors. International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd. |\n\nInt J Robust Nonlinear Control. 2025;35:2835–[2859. wileyonlinelibrary.com/journal/rnc](2859. wileyonlinelibrary.com/journal/rnc)  2835  \n1  INTRODUCTION  \nAdvanced optimal control and estimation problems such as model predictive control, moving horizon estimation, and Kalman filters require models representing, for example, the system dynamics, constraints, reference values, and objective functions. Based on these models, the inputs, the estimates of the states, or parameters are found based on the minimization of an objective function1,2 Often obtaining accurate models is challenging. For example, dynamical models can be constructed with first principles, such as conservation of energy, matter, and thermodynamics laws. However, underlying unknown nonlineariti","cbCaiqvlAkecGkY4","https://ap.wps.com/l/cbCaiqvlAkecGkY4","pdf",2104464,1,25,"English","en",105,"# Introduction\n## Background: model-based control and estimation\n## Challenges in obtaining accurate models\n## Data-driven and machine-learning models for control","[{\"question\":\"What problems does HILO-MPC target in optimal control and estimation?\",\"answer\":\"HILO-MPC supports machine-learning-supported optimization, model predictive control, and estimation, including moving horizon estimation and Kalman filtering.\"},{\"question\":\"How does the toolbox integrate machine learning with model-based methods?\",\"answer\":\"It uses state-of-the-art machine-learning libraries to train components used to define the optimization/control or estimation problem.\"},{\"question\":\"Why is HILO-MPC considered flexible and useful beyond research?\",\"answer\":\"Its simplicity, adaptability, and numerous implemented examples make it suitable for fundamental development tasks and as a teaching tool.\"}]","Flexible development and evaluation of machine-learning-supported optimal control and estimation methods via HILO-MPC | 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