  We consider the problem of stabilization of a linear system, under state and
control constraints, and subject to bounded disturbances and unknown parameters
in the state matrix. First, using a simple least square solution and available
noisy measurements, the set of admissible values for parameters is evaluated.
Second, for the estimated set of parameter values and the corresponding linear
interval model of the system, two interval predictors are recalled and an
unconstrained stabilizing control is designed that uses the predicted
intervals. Third, to guarantee the robust constraint satisfaction, a model
predictive control algorithm is developed, which is based on solution of an
optimization problem posed for the interval predictor. The conditions for
recursive feasibility and asymptotic performance are established. Efficiency of
the proposed control framework is illustrated by numeric simulations.
