  We propose a robust adaptive Model Predictive Control (MPC) strategy with
online set-based estimation for constrained linear systems with unknown
parameters and bounded disturbances. A sample-based test applied to predicted
trajectories is used to ensure convergence of parameter estimates by enforcing
a persistence of excitation condition on the closed loop system. The control
law robustly satisfies constraints and has guarantees of feasibility and
input-to-state stability. Convergence of parameter set estimates to the actual
system parameter vector is guaranteed under conditions on reachability and
tightness of disturbance bounds.
