Stabilizing embedded MPC with computational complexity guarantees

Matteo Rubagotti, Panagiotis Patrinos, Alberto Bemporad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper describes a model predictive control (MPC) approach for discrete-time linear systems with hard constraints on control and state variables. The finite-horizon optimal control problem is formulated as a quadratic program (QP), and solved using a recently proposed dual fast gradient-projection method. More precisely, in a finite number of iterations of the mentioned optimization algorithm, a solution with bounded levels of infeasibility and suboptimality is determined for an alternative problem. This solution is shown to be a feasible suboptimal solution for the original problem, leading to exponential stability of the closed-loop system. The proposed strategy is particularly useful in embedded control applications, for which real-time constraints and limited computing resources can impose tight bounds on the possible number of iterations that can be performed within the scheduled sampling time.

Original languageEnglish
Title of host publication2013 European Control Conference, ECC 2013
PublisherIEEE Computer Society
Pages3065-3070
Number of pages6
ISBN (Print)9783033039629
DOIs
Publication statusPublished - Jan 1 2013
Event2013 12th European Control Conference, ECC 2013 - Zurich, Switzerland
Duration: Jul 17 2013Jul 19 2013

Publication series

Name2013 European Control Conference, ECC 2013

Conference

Conference2013 12th European Control Conference, ECC 2013
CountrySwitzerland
CityZurich
Period7/17/137/19/13

ASJC Scopus subject areas

  • Control and Systems Engineering

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