Abstract: This talk will present a first-principles approach to online nonstochastic control, an emerging paradigm in control of dynamical systems that gives provable learning-theory-inspired performance guarantees without making any distributional assumptions. The traditional approaches to control present a present a dichotomy between planning for the average case (stochastic) and the worst case (robust control). Instead, in this framework, the learner's objective is to ensure small regret in comparison to the best controller in hindsight from a suitably chosen benchmark class, thus delivering near-optimal performance on both worst- and average-case instances in a unified way. This talk will introduce the basic theory of non-stochastic control, including extensions to partially observed and unknown systems. Towards the end, we will survey more recent developments, encompassing faster algorithms and extensions to nonlinear systems.