A multi-level fuzzy switching control method based on fuzzy multi-model and its application for PWR core power control

被引：9

作者：

Zeng, Wenjie ^{[1
,2
]}

Jiang, Qingfeng ^{[1
]}

Liu, Yinuo ^{[1
]}

Xie, Jinsen ^{[1
]}

Yu, Tao ^{[1
]}

机构：

[1] Univ South China, Sch Nucl Sci & Technol, Hengyang City 421001, Peoples R China

[2] China Inst Atom Energy, Dept Reactor Engn, Beijing 102413, Peoples R China

来源：

PROGRESS IN NUCLEAR ENERGY | 2021年 / 138卷

基金：

中国国家自然科学基金;

关键词：

Fuzzy multi-model; Multi-level fuzzy switching controller; T-S fuzzy Rule; PWR core Power; PID CONTROL; REACTOR;

D O I：

10.1016/j.pnucene.2021.103743

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

As the characteristics of complex system are usually multivariable, nonlinear, and wide operating range, it is difficult to achieve ideal control of complex nonlinear systems. To solve the problem, a multi-level fuzzy switching control method based on fuzzy multi-model is designed. The PD controller, the fuzzy controller and the fuzzy-PID controller are selected as the sub-controllers, and T-S fuzzy rules are used to realize smooth switching between sub-controllers. The multi-level fuzzy switching controller is designed as the parent controller. In this paper, the local model of PWR core is established by selecting five linear models at power levels of 20% FP, 40% FP, 60% FP, 80% FP and 100% FP, and the fuzzy multi-model of PWR core is established by weighting the five local models with triangular membership function. Then, the multi-level fuzzy switching controller with the core fuzzy multi-model is used for core power control, and the power control simulation of PWR core is carried out. The results show that the multi-level fuzzy switching controller designed based on the core fuzzy multi-model is suitable for PWR core power control.

引用

页数：12

共 50 条

[21] Nonlinear Systems Identification and Control Based on Fuzzy-neural Multi-model
Qi, Wei-min
Zhang, Xia
PROCEEDINGS OF THE 2013 8TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2013), 2013, : 773 - 777
[22] Multi-model control of blast furnace burden surface based on fuzzy SVM
Li, Xiao-Li
Liu, De-Xin
Jia, Chao
Chen, Xian-zhong
NEUROCOMPUTING, 2015, 148 : 209 - 215
[23] Fuzzy model based on multi-level fuzzy information granulation for regression estimation
Wang, L. (linda_gh@sina.com), 1600, Binary Information Press, P.O. Box 162, Bethel, CT 06801-0162, United States (09):
[24] Fuzzy switching multi-model control for the aerospace vehicle's re-entry attitude
Qian, Cheng-Shan
Wu, Qing-Xian
Jiang, Chang-Sheng
Wang, Yu-Hui
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2007, 29 (11): : 1912 - 1916
[25] Multi-Model Switching Control Based on Dynamical Model Bank
Zhai Junyong
PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE, 2010, : 3458 - 3462
[26] Dynamic matrix control based on multi-model synthesis and its application
Mo Xiaoming
Proceedings of the 26th Chinese Control Conference, Vol 6, 2007, : 167 - 170
[27] A new smooth switching strategy for multi-model control and its application on A BTT missile
Duan, GuangRen
Tan, Feng
ISSCAA 2006: 1ST INTERNATIONAL SYMPOSIUM ON SYSTEMS AND CONTROL IN AEROSPACE AND ASTRONAUTICS, VOLS 1AND 2, 2006, : 1392 - +
[28] Experimental Investigations of Axial Force Control for Friction Stir Welding Based on Multi-Level Fuzzy Control
Wang Qingxia
Ge Zengwen
Wu Chongjun
Ren Fei
Guo Lijie
CONFERENCE PROCEEDINGS OF 2019 5TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2019, : 436 - 440
[29] Multi-model And Fuzzy PID Control for Fixed-wing UAV
Zhou, Dali
Geng, Qingbo
PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS, ROBOTICS AND AUTOMATION (ICMRA 2015), 2015, 15 : 523 - 528
[30] A fuzzy-neural multi-model for mechanical systems identification and control
Baruch, IS
Beltran, R
Garrido, R
Gortcheva, E
2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 2113 - 2119

← 1 2 3 4 5 →