Constrained Nonlinear Output Regulation Using Model Predictive Control

We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of proposed is that application does not require solution classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure. In particular, formulation simply minimizes predicted error, possibly with some input regularization. Instead using terminal cost/sets positive definite stage cost as standard in MPC theory, we build on theoretical results by Grimm et al. 2005 detectability notion. applicable if problem (strictly) feasible, plant incrementally stabilizable and input-output state stable (i-IOSS/detectable). show for minimum phase systems such ensures exponential stability manifold. also provide procedure case unstable zero dynamics an incremental regularization nonresonance condition. Inherent robustness properties noisy error/output-feedback are established under simplifying assumptions (e.g. no constraints). illustrated example involving offset free tracking error feedback. paper contains novel without constraints semidefinite input/output costs independent interest.