**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.

Corresponding technical report: pdf file (192 KB)

@inproceedings{NecNed:13-038,

author={I. Necoara and V. Nedelcu and T. Keviczky and M.D. Doan and B. {D}e Schutter},

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}

}

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