Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

被引：30
|
作者：
Zou, Tao [1 ,3 ]
Wu, Sheng [1 ,2 ]
Zhang, Ridong [1 ,2 ]
机构：
[1] Hangzhou Dianzi Univ, Sch Automat, Belt & Rd Informat Res Inst, Hangzhou 310018, Zhejiang, Peoples R China
[2] Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China
[3] Chinese Acad Sci, Shenyang Inst Automat, Dept Informat Serv & Intelligent Control, Key Lab Networked Control Syst, Shenyang 110016, Liaoning, Peoples R China
来源：
ISA TRANSACTIONS | 2018年 / 73卷
基金：
中国国家自然科学基金;
关键词：
Model predictive fault-tolerant control; Batch processes; Genetic algorithm; Partial actuator failure; ITERATIVE LEARNING CONTROL; DIAGNOSIS; DESIGN; FEEDBACK;
D O I：
10.1016/j.isatra.2017.12.022
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：147 / 153
页数：7
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