Constrained nonlinear output regulation using model predictive control -- extended version

Understand

We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem.

• The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis-Byrnes-Isidori) equations or any other offline design procedure.
• In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization.
• Instead of using terminal cost/sets or a positive definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al.