**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* (R.R. Negenborn and J.M. Maestre,
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.

Online version of the chapter

Corresponding technical report: pdf file (138 KB)

@incollection{DoaKev:13-001,

author={M.D. Doan and T. Keviczky and B. {D}e Schutter},

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={R.R. Negenborn and J.M. Maestre},

publisher={Springer},

address={Dordrecht, The Netherlands},

pages={393--406},

year={2014},

doi={10.1007/978-94-007-7006-5_24}

}

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