Sequential distributed model predictive control for state-dependent nonlinear systems

Salah G. Abokhatwa, Reza Katebi

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

Abstract

In this paper, sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a statedependent nonlinear model to avoid the complexity of the nonlinear programming (NLP) problem. In this distributed framework, local MPCs solve convex optimization problem and exchange information via one directional communication channel at each sampling time to achieve the global control objectives of the system. Numerical simulation results show that the performance of the proposed DMPC algorithms is close to the centralized NMPC but computationally more efficient compared to the centralized one.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages565-570
Number of pages6
ISBN (Print)9780769551548
DOIs
Publication statusPublished - 1 Dec 2013
Event2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 - Manchester, United Kingdom
Duration: 13 Oct 201316 Oct 2013

Conference

Conference2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
CountryUnited Kingdom
CityManchester
Period13/10/1316/10/13

Keywords

  • centralized model predictive control
  • distributed model predictive control
  • nonlinear state-dependent control
  • supervisory model predictive control

Fingerprint Dive into the research topics of 'Sequential distributed model predictive control for state-dependent nonlinear systems'. Together they form a unique fingerprint.

Cite this