Supply Chain Simulation with Switching Adaptive Model Predictive Control Methodology

DOI

10.2991/icsnce-16.2016.124How to use a DOI?

Keywords

Model predictive control; Optimization method; Switching control; Across-chain coordination

Abstract

An adaptive multiple model predictive control (MMPC) method for an uncertain input-constrained neutrally stable supply chains system with control-relevant switching is presented. By employing an input-to-state stabilising MPC as the multi-controller, switching adaptive MMPC is proposed for the system. Unlike previous methods for handling uncertainties on the basis of minmax MPC laws or techniques for linear parameter varying systems, the proposed MPC scheme employs model switching to deal with modelling uncertainties through adaptation; a best model is selected for the MPC law from time to time. The proposed scheme using finite prediction horizon guarantees global stability. Simulation results are given to show the effectiveness of the scheme.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunling Liu
AU  - Jizi Li
AU  - Junfeng Wang
AU  - Yangjie Tian
PY  - 2016/07
DA  - 2016/07
TI  - Supply Chain Simulation with Switching Adaptive Model Predictive Control Methodology
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 639
EP  - 646
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.124
DO  - 10.2991/icsnce-16.2016.124
ID  - Liu2016/07
ER  -