  We develop an indirect-adaptive model predictive control algorithm for
uncertain linear systems subject to constraints. The system is modeled as a
polytopic linear parameter varying system where the convex combination vector
is constant but unknown. Robust constraint satisfaction is obtained by
constraints enforcing a robust control invariant. The terminal cost and set are
constructed from a parameter-dependent Lyapunov function and the associated
control law. The proposed design ensures robust constraint satisfaction and
recursive feasibility, is input-to-state stable with respect to the parameter
estimation error and it only requires the online solution of quadratic
programs.
