Iterative learning hybrid robust predictive fault-tolerant control for nonlinear batch processes with partial actuator faults

被引：13

作者：

Li, Hui ^{[1
]}

Wang, Shiqi ^{[1
]}

Shi, Huiyuan ^{[2
,3
]}

Su, Chengli ^{[2
]}

Li, Ping ^{[1
]}

机构：

[1] Univ Sci & Technol Liaoning, Sch Elect & Informat Engn, Anshan 114051, Peoples R China

[2] Liaoning Petrochem Univ, Sch Informat & Control Engn, Fushun 113001, Peoples R China

[3] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang 110819, Peoples R China

来源：

JOURNAL OF PROCESS CONTROL | 2023年 / 129卷

基金：

中国国家自然科学基金;

关键词：

Robust predictive control; Iterative learning control; Batch process; T-S fuzzy; Partial actuator faults; SYSTEMS; DESIGN; DELAY;

D O I：

10.1016/j.jprocont.2023.103054

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For nonlinear batch processes with uncertainties, disturbances and partial actuator faults, an iterative learning robust predictive fault-tolerant control approach is developed. Based on the conclusion of robustness analysis, the stability conditions combining the robust positive invariant set with the terminal constraint set can guarantee that the system is stable under faults. Furthermore, the real-time optimal gains of control law solved online can significantly reduce the learning cycles of the controller, which in turn can meet the demand for rapid production of batch processes. The injection process is employed as an example to ultimately confirm the viability and efficacy of the developed approach.& COPY; 2023 Elsevier Ltd. All rights reserved

引用

页数：10

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