Reference
I. Necoara, V. Nedelcu, T. Keviczky,
M. D.
Doan, and B. De Schutter, "Linear model predictive control based on approximate
optimal control inputs and constraint tightening,"
Proceedings
of the 52nd IEEE Conference on Decision and Control, Florence, Italy,
pp. 7728-7733, Dec. 2013.
Abstract
In this paper we propose a model predictive control scheme for discrete-time
linear time-invariant systems based on inexact numerical optimization
algorithms. We assume that the solution of the associated quadratic program
produced by some numerical algorithm is possibly neither optimal nor feasible,
but the algorithm is able to provide estimates on primal suboptimality and
primal feasibility violation. By tightening the complicating constraints we can
ensure the primal feasibility of the approximate solutions generated by the
algorithm. Finally, we derive a control strategy that has the following
properties: the constraints on the states and inputs are satisfied, asymptotic
stability of the closed-loop system is guaranteed, and the number of iterations
needed for a desired level of suboptimality can be determined.
Publisher page
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BibTeX
@inproceedings{NecNed:13-038,
author = {Necoara, Ion and Nedelcu, Valentin and Keviczky, Tam{\'a}s and
Doan, Minh Dang and De Schutter, Bart},
title = {Linear Model Predictive Control Based on Approximate Optimal
Control Inputs and Constraint Tightening},
booktitle = {Proceedings of the 52nd IEEE Conference on Decision and
Control},
address = {Florence, Italy},
pages = {7728--7733},
month = dec,
year = {2013}
}