Nominally robust model predictive control with state constraints

doi:10.1109/TAC.2007.906187

Return

Nominally robust model predictive control with state constraints

2007-10-01

PRE

G. Grimm *

S. Emre Tuna

Andrew R. Teel

DOI：10.1109/TAC.2007.906187

Original

Original request for help

Save

Abstract

En 中文

In this paper, we present robust stability results for constrained discrete-time nonlinear systems using a finite-horizon model predictive control (MPC) algorithm for which we do not require the terminal cost to have any particular properties. We introduce a definition that attempts to characterize the robustness properties of the MPC optimization problem. We assume the systems under consideration satisfy this definition (for which we give sufficient conditions) and make two further assumptions. These are that the value function is bounded by a kappa(infinity) function of a state measure (related to the distance from the state to some target set) and that this measure is detectable from the stage cost used in the MPC algorithm. We show that these assumptions lead to stability that is robust to sufficiently small disturbances. While in general the results are semiglobal and practical, when the detectability and upper bound assumptions are satisfied with linear kappa(infinity) functions, the stability and robustness are either semiglobal or global (with respect to the feasible set). We discuss algorithms employing terminal inequality constraints and also provide a specific example of an algorithm that employs a terminal equality constraint.

Keywords:

discrete-time systems

nonlinear model predictive control (MPC)

robustness

receding horizon control

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.