Adaptive model predictive control for constrained linear systems

A novel adaptive output feedback control technique for uncertain linear systems is proposed, able to cope with input and output constraints and measurement noise. At each time step, the collected input-output data are exploited to refine the set of models that are consistent with the available information on the system. Then, the control input is computed according to a receding horizon strategy, which guarantees recursive constraint satisfaction for all the admissible models, hence also for the actual plant. The technique relies only on the solution of linear and quadratic programs. The effectiveness of the approach is illustrated in a numerical example.