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Lyapunov‐based model predictive control of nonlinear systems subject to time‐varying measurement delays
Author(s) -
Liu Jinfeng,
Muñoz de la Peña David,
Christofides Panagiotis D.,
Davis James F.
Publication year - 2009
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1085
Subject(s) - model predictive control , control theory (sociology) , lyapunov function , controller (irrigation) , nonlinear system , computer science , asynchronous communication , stability (learning theory) , lyapunov stability , lyapunov redesign , lyapunov optimization , control engineering , control (management) , engineering , artificial intelligence , machine learning , computer network , physics , quantum mechanics , agronomy , biology
In this work, we focus on model predictive control of nonlinear systems subject to time‐varying measurement delays. The motivation for studying this control problem is provided by networked control problems and the presence of time‐varying delays in measurement sampling in chemical processes. We propose a Lyapunov‐based model predictive controller that is designed taking time‐varying measurement delays explicitly into account, both in the optimization problem formulation and in the controller implementation. The proposed predictive controller allows for an explicit characterization of the stability region and guarantees that the closed‐loop system in the presence of time‐varying measurement delays is ultimately bounded in a region that contains the origin if the maximum delay is smaller than a constant that depends on the parameters of the system and the Lyapunov‐based controller that is used to formulate the optimization problem. The application of the proposed Lyapunov‐based model predictive control method is illustrated using a nonlinear chemical process example with asynchronous, delayed measurements and its stability and performance properties are illustrated to be superior to the ones of two existing Lyapunov‐based model predictive controllers. Copyright © 2008 John Wiley & Sons, Ltd.