Reference
M. D. Doan, T. Keviczky, and B. De
Schutter, "A hierarchical MPC approach with guaranteed feasibility for
dynamically coupled linear systems," in
Distributed Model
Predictive Control Made Easy (
J. M.
Maestre and
R. R. Negenborn, eds.), vol.
69 of
Intelligent Systems, Control and Automation: Science and
Engineering, Dordrecht, The Netherlands: Springer, ISBN
978-94-007-7005-8, pp. 393-406, 2014.
Abstract
In this chapter we describe an iterative two-layer hierarchical approach to MPC
of large-scale linear systems subject to coupled linear constraints. The
algorithm uses constraint tightening and applies a primal-dual iterative
averaging procedure to provide feasible solutions in every sampling step. This
helps overcome typical practical issues related to the asymptotic convergence
of dual decomposition based distributed MPC approaches. Bounds on constraint
violation and level of suboptimality are provided. The method can be applied to
large-scale MPC problems that are feasible in the first sampling step and for
which the Slater condition holds (i.e., there exists a solution that strictly
satisfies the inequality constraints). Using this method, the controller can
generate feasible solutions of the MPC problem even when the dual solution does
not reach optimality, and closed-loop stability is also ensured using bounded
suboptimality.
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BibTeX
@incollection{DoaKev:13-001,
author = {Doan, Minh Dang and Keviczky, Tam{\'a}s and De Schutter, Bart},
title = {A Hierarchical {MPC} Approach with Guaranteed Feasibility for
Dynamically Coupled Linear Systems},
booktitle = {Distributed Model Predictive Control Made Easy},
series = {Intelligent Systems, Control and Automation: Science and
Engineering},
volume = {69},
editor = {Maestre, Jos\'{e} M. and Negenborn, Rudy R.},
publisher = {Springer},
address = {Dordrecht, The Netherlands},
pages = {393--406},
year = {2014}
}