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

Abstract

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. [1] using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible, the plant is incrementally stabilizable and incrementally input-output to state stable (i-IOSS/detectable). We show that for minimum phase systems such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. Inherent robustness properties for the noisy error/output-feedback case are established under simplifying assumptions (e.g. no state constraints). The theoretical results are illustrated with an example involving offset free tracking with noisy error feedback. The paper also contains novel results for MPC without terminal constraints with positive semidefinite input/output stage costs that are of independent interest.

Robust output feedback model predictive control using online estimation bounds

“…Examples of particular high interest include motion planning with vision based measurements (e.g., robotics and autonomous driving), where collision avoidance needs to be guaranteed despite potentially large uncertainty in the state estimate. In addition, solving the constrained output feedback problem is a preliminary for the constrained output regulation problem [1], which includes offset-free tracking [2] as a special cases. In this paper, we present a model predictive control (MPC) approach to the nonlinear constrained output feedback problem that combines modern robust MPC methodologies with online estimation bounds.…”