Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/37052
Title: Stabilizing Linear Model Predictive Control Under Inexact Numerical Optimization
Authors: Rubagotti, Matteo
Patrinos, P.
Bemporad, A.
First Published: 20-May-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE), United States
Citation: IEEE Transactions on Automatic Control, 2014, 59 (6), pp. 1660-1666
Abstract: This note describes a model predictive control (MPC) formulation for discrete-time linear systems with hard constraints on control and state variables, under the assumption that the solution of the associated quadratic program is neither optimal nor satisfies the inequality constraints. This is common in embedded control applications, for which real-time constraints and limited computing resources dictate restrictions on the possible number of on-line iterations that can be performed within a sampling period. The proposed approach is rather general, in that it does not refer to a particular optimization algorithm, and is based on the definition of an alternative MPC problem that we assume can only be solved within bounded levels of suboptimality, and violation of the inequality constraints. By showing that the inexact solution is a feasible suboptimal one for the original problem, asymptotic or exponential stability is guaranteed for the closed-loop system. Based on the above general results, we focus on a specific dual accelerated gradient-projection method to obtain a stabilizing MPC law that only requires a predetermined maximum number of on-line iterations.
DOI Link: 10.1109/TAC.2013.2293451
ISSN: 0018-9286
Links: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6725631
http://hdl.handle.net/2381/37052
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Appears in Collections:Published Articles, Dept. of Engineering

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