Supply Chain Simulation with Switching Adaptive Model Predictive Control Methodology

被引：0
|
作者：
Liu, Chunling [1 ,2 ]
Li, Jizi [2 ]
Wang, Junfeng [1 ]
Tian, Yangjie [1 ]
机构：
[1] Wuhan Text Univ, Sch Mech Engn & Automat, Wuhan, Peoples R China
[2] Wuhan Text Univ, Supply Chain Syst Res Ctr, Wuhan, Peoples R China
来源：
PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON SENSOR NETWORK AND COMPUTER ENGINEERING | 2016年 / 68卷
关键词：
Model predictive control; Optimization method; Switching control; Across-chain coordination; OPTIMIZATION; SYSTEMS; DESIGN;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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 stabilizing MPC as the multi-controller, switching adaptive MMPC is proposed for the system. Unlike previous methods for handling uncertainties on the basis of minimal MPC laws or techniques for linear parameter varying systems, the proposed MPC scheme employs model switching to deal with modeling 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.
引用
收藏
页码：644 / 651
页数：8
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