Computational Load Reduction in Model Predictive Control of Nonlinear Systems via Decomposition

被引：0
|
作者：
Adelipour, Saeed [1 ]
Rastgar, Mahdi [1 ]
Haeri, Mohammad [1 ]
机构：
[1] Sharif Univ Technol, Elect Engn Dept, Adv Control Syst Lab, Tehran, Iran
来源：
2017 5TH INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, AND AUTOMATION (ICCIA) | 2017年
关键词：
Model predictive control; System decomposition; Computational load; Linear matrix inequality; multi-input nonlinear systems; ALGORITHM; NMPC;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The aim of this study is to reduce the computational load in model predictive control of multi-input nonlinear systems. First, the nonlinear system which has a high number of states and inputs is decomposed into several subsystems by solving a linear integer programming problem offline. Then, the model of each subsystem is revised by considering the effect of coupling and interactions of other subsystems. Next, the robust model predictive technique based on linear matrix inequalities is employed to compute control signal for each subsystem. An industrial chemical reaction example is used to illustrate the effectiveness of the proposed method.
引用
收藏
页码：216 / 221
页数：6
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