报告详情
Predictive Control of Pressure Stabilizer Based on PINN
编号：18
访问权限：仅限参会人
更新：2024-09-05 09:35:46 浏览：94次
口头报告
报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
暂无文件
摘要
To optimize the control of the pressurizer in nuclear power plants, this paper employs Model Predictive Control (MPC) for the optimization of pressure control in the pressurizer. MPC is an advanced control strategy that predicts the future behavior of a system through the establishment of a mathematical model and optimizes the control inputs based on these predictions. In MPC, an accurate system model is essential for predicting future system behavior, making model identification a critical step. This model needs to accurately reflect the dynamic characteristics of the process, including linear or nonlinear, time-varying or non-time-varying features. If the model is inaccurate, the effectiveness of predictive control may be significantly reduced. To enhance the accuracy of modeling, this paper utilizes Physics-Informed Neural Networks (PINNs) for model identification of the pressurizer in nuclear power plants, and then applies MPC on the established predictive model. Since the pressurizer is a nonlinear and time-varying model, using a transfer function for prediction, as in traditional predictive control methods, may not adequately capture its dynamics. The adoption of PINNs for modeling the pressurizer is expected to better reflect its changing process, achieving more precise control.
关键词
Pressurizer Control,MODEL PREDICTIVE CONTROL,Physics-Informed Neural Networks,Model Identification
报告人
Ning Zhu
Harbin Engineering University稿件作者
全部评论
重要日期
-
会议日期
09月23日
2024
至09月25日
2024
-
09月24日 2024
报告提交截止日期
-
09月25日 2024
注册截止日期
主办单位
Harbin Engineering University (HEU)
联系方式
- Prof. WANG Xiang
- xi******@hrbeu.edu.cn
历届会议
-
2016年09月26日 中国 Chengdu,China
8th International Symposium on Symbiotic Nuclear Power Systems for 21st Century -
2016年09月26日 中国 Chengdu,China
第八届21世纪和谐核电系统国际会议 -
2010年08月23日 中国 哈尔滨市
第三届21世纪和谐核电系统国际会议（ISSNP2010）
发表评论