Autonomous systems have become an interconnected part of everyday life with the recent increases in computational power available for both onboard computers and offline data processing. Two main research communities, Optimal Control and Reinforcement Learning stand out in the field of autonomous systems, each with a vastly different perspective on the control problem. While model-based controllers offer stability guarantees and are used in nearly all real-world systems, they require a model of the system and operating environment. The training of learning- based controllers is currently mostly limited to simulators, which also require a model of the system and operating environment. It is not possible to model every possible operating scenario an autonomous system can encounter in the real world at design time and currently, no control methods exist for such scenarios. In this seminar, we present a hybrid control framework, comprised of a learning-based supervisory controller and a set of model-based low-level controllers, that can improve a system’s robustness to unknown operating conditions.

*Speaker:
Yves Sohege, affiliated to the Insight Centre for Data Analytics, University College Cork (UCC), Ireland, is a 26-year-old PhD student at UCC. He have been working remotely from the Netherlands for the past two years. He completed his BSc in Computer Science in UCC in 2016 during which he was nominated for Graduate of the Year and received the Quercus Scholarship for Academic Performance. His research interests are focused around Reinforcement Learning and fault-tolerant control, specifically focusing on unknown operating conditions and quadcopter control.