Parallel MPC for Linear Systems with Input Constraints

Authors

Y. Jiang, J. Oravec, B. Houska, and M. Kvasnica

Reference

IEEE Transactions on Automatic Control,
Volume 66(7), pages 3401 - 3408, 2021.

Abstract

This paper is about a real-time model predictive control (MPC) algorithm for large-scale, structured linear systems with polytopic control constraints. The proposed controller receives the current state measurement as an input and computes a sub-optimal control reaction by evaluating a finite number of piecewise affine functions that correspond to the explicit solution maps of small-scale parametric quadratic programming (QP) problems. We provide asymptotic stability guarantees, which can be verified offline. The feedback controller is suboptimal on purpose, because we are enforcing real-time requirements assuming that it is neither possible to solve the given large-scale QP nor to enforce feasibility in the given amount of time. Here, a key contribution of this paper is that we provide a bound on the sub-optimality of the controller. The approach is illustrated by benchmark case studies.

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Bibtex

@ARTICLE{Jiang2021,
author = {Jiang, Y. and Oravec, J. and Houska, B. and Kvasnica, M.},
title = {Parallel MPC for Linear Systems with Input Constraints},
journal = {IEEE Transactions on Automatic Control},
year = {2021},
volume = {66},
number = {7},
pages = {3401–3408}
}