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Cooperative control of dynamically decoupled systems via distributed model predictive control
Author(s) -
Müller Matthias A.,
Reble Marcus,
Allgöwer Frank
Publication year - 2012
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.2826
Subject(s) - computer science , model predictive control , convergence (economics) , a priori and a posteriori , synchronization (alternating current) , set (abstract data type) , control (management) , task (project management) , control theory (sociology) , nonlinear system , function (biology) , distributed computing , artificial intelligence , engineering , computer network , philosophy , channel (broadcasting) , physics , systems engineering , epistemology , quantum mechanics , evolutionary biology , economics , biology , programming language , economic growth
SUMMARY In this paper, we propose a general framework for distributed model predictive control of discrete‐time nonlinear systems with decoupled dynamics but subject to coupled constraints and a common cooperative task. To ensure recursive feasibility and convergence to the desired cooperative goal, the systems optimize a local cost function in a sequential order, whereas only neighbor‐to‐neighbor communication is allowed. In contrast to most of the existing distributed model predictive control schemes in the literature, we do not necessarily consider the stabilization of an a priori known set point. Instead, also other cooperative control tasks such as consensus and synchronization problems can be handled within the proposed framework. In particular, one of our main contributions is to show how for the latter case the terminal cost functions and the terminal region can be suitably defined and computed. Furthermore, we illustrate our results with simulation examples. Copyright © 2012 John Wiley & Sons, Ltd.