  This paper presents an uncertainty compensation-based robust adaptive model
predictive control (MPC) framework for linear systems with both matched and
unmatched nonlinear uncertainties subject to both state and input constraints.
In particular, the proposed control framework leverages an L1 adaptive
controller (L1AC) to compensate for the matched uncertainties and to provide
guaranteed uniform bounds on the error between the states and control inputs of
the actual system and those of a nominal i.e., uncertainty-free, system. The
performance bounds provided by the L1AC are then used to tighten the state and
control constraints of the actual system, and a model predictive controller is
designed for the nominal system with the tightened constraints. The proposed
control framework, which we denote as uncertainty compensation-based MPC
(UC-MPC), guarantees constraint satisfaction and achieves improved performance
compared with existing methods. Simulation results on a flight control example
demonstrate the benefits of the proposed framework.
