Balancing safety and performance in learning-based optimal control
In this talk, we summarize some recent developments from our group towards the balancing of safety and performance in learning-based optimal control. We connect them to the broader literature on the topic. We will illustrate the properties of the described methodologies using applications from autonomous driving. As the highlighted methodologies typically lead to large-scale optimization problems requiring real-time solutions on embedded hardware, we finally present solvers tailored towards these applications.
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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.
Teacher / speaker
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. Join us for this one hour webinar series!
