Robust Adaptive Tube Model Predictive Control

An adaptive Model Predictive Control (adaptive MPC) strategy is proposed for linear systems with constant unknown model parameters, bounded additive disturbances and state and control constraints. By combining online set-based identification and robust tube MPC, the proposed controller reduces the conservativeness of constraint handling, guarantees recursive feasibility and provides asymptotic bounds on the closed loop system state that depend explicitly on the the identified parameter set. Computational tractability is ensured by using fixed complexity polytopic sets to bound the model parameters and predicted states. Convex conditions for persistence of excitation are considered. The results are illustrated by a numerical example.