Veiligheid en performantie in learning-based optimal control
In deze presentatie geven we een samenvatting van enkele recente ontwikkelingen vanuit onze groep op het gebied van het balanceren van veiligheid en prestaties in learning-based optimal control. We verbinden deze met de bredere literatuur op dit gebied. We zullen de eigenschappen van de beschreven methoden illustreren met behulp van toepassingen uit het autonoom rijden. Aangezien de benadrukte methoden doorgaans leiden tot grootschalige optimalisatieproblemen die real-time oplossingen op ingebedde hardware vereisen, presenteren we uiteindelijk solvers die zijn aangepast voor deze toepassingen.
Praktische info:
Leertraject
Inschrijven?
- Prijs: gratis
- Webinars vinden plaats één keer per maand. Registratie is niet nodig, je kan zo naar het seminarie komen.
Krijg je graag een maandelijkse herinnering aan de seminaries in deze reeks over tijdsreeksen? Schrijf je in!
georganiseerd door:
Modern day decision-making and control systems are required to operate with increasing levels of autonomy and under highly uncertain conditions. This brings about the need for advanced planning and control strategies that are able to learn from online observations, while still adhering to strict requirements on safety, reliability and real-time capabilities, common in more traditional control applications. To this end, many approaches are emerging, differing in their underlying assumptions on sources of uncertainty and methodologies for learning.
Lesgever/spreker
Panagiotis (Panos) Patrinos is associate professor at the Department of Electrical Engineering (ESAT) of KU Leuven, Belgium. In 2014 he was a visiting professor at Stanford. He received his PhD in Control and Optimization, M.S. in Applied Mathematics and M.Eng. from the National Technical University of Athens in 2010, 2005 and 2003, respectively. After his PhD he held postdoc positions at the University of Trento and IMT Lucca, Italy, where he became an assistant professor in 2012. His current research interests are in the theory and algorithms of structured convex and nonconvex optimization and predictive control with a focus on large-scale, distributed, stochastic and embedded optimization and a wide range of application areas including automotive, aerospace, machine learning, signal processing and energy.
Machine Learning in Control and Automation Webinar Series
In this webinar series, we bring together researchers that are interested in, or conducting research on Machine Learning for control and automation purposes. A variety of techniques and their applications will be covered, ranging from traditional machine learning techniques such as System Identification, State Estimation and Model Predictive Control (MPC) to Deep Neural Networks (DNN)-based approaches and Reinforcement Learning. We offer a varied program of national and international speakers.
