We study the control of a linear dynamical system with adversarial
disturbances (as opposed to statistical noise). The objective we consider is
one of regret: we desire an online control procedure that can do nearly as well
as that of a procedure that has full knowledge of the disturbances in
hindsight. Our main result is an efficient algorithm that provides nearly tight
regret bounds for this problem. From a technical standpoint, this work
generalizes upon previous work in two main aspects: our model allows for
adversarial noise in the dynamics, and allows for general convex costs.