  Recent advances in learning for control allow to synthesize vehicle
controllers from learned system dynamics and maintain robust stability
guarantees. However, no approach is well-suited for training linear
time-invariant (LTI) controllers using arbitrary learned models of the
dynamics. This article introduces a method to do so. It uses a robust control
framework to derive robust stability criteria. It also uses simulated policy
rollouts to obtain gradients on the controller parameters, which serve to
improve the closed-loop performance. By formulating the stability criteria as
penalties with computable gradients, they can be used to guide the controller
parameters toward robust stability during gradient descent. The approach is
flexible as it does not restrict the type of learned model for the simulated
rollouts. The robust control framework ensures that the controller is already
robustly stabilizing when first implemented on the actual system and no data is
yet collected. It also ensures that the system stays stable in the event of a
shift in dynamics, given the system behavior remains within assumed uncertainty
bounds. We demonstrate the approach by synthesizing a controller for simulated
autonomous lane change maneuvers. This work thus presents a flexible approach
to learning robustly stabilizing LTI controllers that take advantage of modern
machine learning techniques.
