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DTSTAMP:20221202T170636
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SUMMARY:Talk of Prof. Ali Mesbah
DESCRIPTION:Prof. Ali Mesbah\nDepartment of Chemical and Biomolecular Engineering\nUniversity of California at Berkeley \nBerkeley, CA, United States  \nThursday 2022-12-15 4 p.m.\nIST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen  \nAbstract\nThe closed-loop performance of model-based controllers, such as model predictive control, is\nhighly dependent on the choice of prediction models, controller formulation, and tuning parameters.\nHowever, prediction models are typically optimized for prediction accuracy, instead of performance,\nand MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this talk, we\ndiscuss a general approach for performance-oriented model learning and automated tuning of\nmodel-based controllers with arbitrary structure under uncertainty. We formulate the auto-tuning\nproblem as a black-box optimization problem that can be tackled with derivative-free, Bayesian\noptimization (BO). In particular, we discuss how system uncertainties can be handled systematically\nin BO in order to ensure robust model learning and controller tuning using closed-loop performance\ndata. We demonstrate the application of the aforementioned BO methods in the context of\nbiomanufacturing systems for deep space missions, as well as biomedical systems.  \nBiographical Information\nAli Mesbah is Associate Professor of Chemical and Biomolecular Engineering at the University of\nCalifornia at Berkeley. Before joining UC Berkeley, Dr. Mesbah was a senior postdoctoral associate\nat MIT. He holds a Ph.D. degree in Systems and Control from Delft University of Technology. Dr.\nMesbah is a senior member of the IEEE and AIChE. He serves on the IEEE Control Systems Society\nConference Editorial Board and IEEE Control Systems Society Technology Conference Editorial Board,\nand is a subject editor of Optimal Control Applications and Methods and IEEE Transactions on\nRadiation and Plasma Medical Sciences. Dr. Mesbah is recipient of the Best Application Paper Award\nof the IFAC World Congress in 2020, the AIChE's 35 Under 35 Award in 2017, the IEEE Control Systems\nOutstanding Paper Award in 2017, and the AIChE CAST W. David Smith, Jr. Publication Award in 2015.\nHis research interests lie at the intersection of optimal control, machine learning, and applied\nmathematics, with applications to learning-based analysis, diagnosis, and predictive control of\nmaterials processing and manufacturing systems. 
DTSTART;TZID=Europe/Berlin;VALUE=DATE:20221215
URL;VALUE=URI:https://www.ist.uni-stuttgart.de/events/Talk-of-Prof-00001.-Ali-Mesbah/
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