|Time:||December 15, 2022|
|Download as iCal:||
Prof. Ali Mesbah
Department of Chemical and Biomolecular Engineering
University of California at Berkeley
Berkeley, CA, United States
Thursday 2022-12-15 4 p.m.
IST Seminar Room 2.255 - Pfaffenwaldring 9 - Campus Stuttgart-Vaihingen
The closed-loop performance of model-based controllers, such as model predictive control, is highly dependent on the choice of prediction models, controller formulation, and tuning parameters. However, prediction models are typically optimized for prediction accuracy, instead of performance, and MPC tuning is typically done manually to satisfy (probabilistic) constraints. In this talk, we discuss a general approach for performance-oriented model learning and automated tuning of model-based controllers with arbitrary structure under uncertainty. We formulate the auto-tuning problem as a black-box optimization problem that can be tackled with derivative-free, Bayesian optimization (BO). In particular, we discuss how system uncertainties can be handled systematically in BO in order to ensure robust model learning and controller tuning using closed-loop performance data. We demonstrate the application of the aforementioned BO methods in the context of biomanufacturing systems for deep space missions, as well as biomedical systems.
Ali Mesbah is Associate Professor of Chemical and Biomolecular Engineering at the University of California at Berkeley. Before joining UC Berkeley, Dr. Mesbah was a senior postdoctoral associate at MIT. He holds a Ph.D. degree in Systems and Control from Delft University of Technology. Dr. Mesbah is a senior member of the IEEE and AIChE. He serves on the IEEE Control Systems Society Conference Editorial Board and IEEE Control Systems Society Technology Conference Editorial Board, and is a subject editor of Optimal Control Applications and Methods and IEEE Transactions on Radiation and Plasma Medical Sciences. Dr. Mesbah is recipient of the Best Application Paper Award of the IFAC World Congress in 2020, the AIChE's 35 Under 35 Award in 2017, the IEEE Control Systems Outstanding Paper Award in 2017, and the AIChE CAST W. David Smith, Jr. Publication Award in 2015. His research interests lie at the intersection of optimal control, machine learning, and applied mathematics, with applications to learning-based analysis, diagnosis, and predictive control of materials processing and manufacturing systems.