Control Meets Learning Seminar

Adaptive control is a field thanks to whose six decades of activity many challenges in combining feedback control with online learning have been either overcome or understood to be insurmountable. As such, the results in this field are not only a veritable checklist of properties that future learning-based control methods, including those related to reinforcement learning, should strive to guarantee, but also a checklist of properties demonstrated or deemed hopeless. I will touch on learning-based feedback designs across the entire spectrum in terms of reliance on models - from the conventional model-based adaptive control, to non-model-based RL, to the entirely model-free "extremum seeking." I focus on fundamental questions but illustrate them with examples from robotics, aquatic locomotion, road traffic, and semiconductor manufacturing.