  In this work, we develop a method based on robust control techniques to
synthesize robust time-varying state-feedback policies for finite, infinite,
and receding horizon control problems subject to convex quadratic state and
input constraints. To ensure constraint satisfaction of our policy, we employ
(initial state)-to-peak gain techniques. Based on this idea, we formulate
linear matrix inequality conditions, which are simultaneously convex in the
parameters of an affine control policy, a Lyapunov function along the
trajectory and multiplier variables for the uncertainties in a time-varying
linear fractional transformation model. In our experiments this approach is
less conservative than standard tube-based robust model predictive control
methods.
