Dynamic Data Reconciliation to Decrease the Effect of Measurement Noise on Controller Performance Assessment-Reference-Cited by-同舟云学术

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Dynamic Data Reconciliation to Decrease the Effect of Measurement Noise on Controller Performance Assessment

Published:2020-05 Issue:5 Volume:15 Page:714-722
ISSN:1931-4973
Container-title:IEEJ Transactions on Electrical and Electronic Engineering
language:en
Short-container-title:IEEJ Trans Elec Electron Eng

Author:

Yang Guanghui¹,Zhang Zhengjiang¹,Zhao Sheng²,Zhu Wangwang¹,Chen Chong²

Affiliation:

1. The National and Local Joint Engineering Laboratory of Electrical Digital Design TechnologyWenzhou University Wenzhou Zhejiang 325000 China

2. The Key Laboratory of Low‐Voltage Apparatus Intellectual Technology of ZhejiangWenzhou University Wenzhou Zhejiang 325000 China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23107

Reference37 articles.

1. A practical approach for large-scale controller performance assessment, diagnosis, and improvement

2. A New Pattern Recognition Adaptive Controller with Application to HVAC Systems

3. Process and controller performance monitoring: overview with industrial applications

4. An overview of control performance assessment technology and industrial applications

5. Assessment of control loop performance

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1. Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation;Measurement Science and Technology;2024-07-30

2. Gaussian process regression combined with dynamic data reconciliation for improving the performance of nonlinear dynamic systems;Nonlinear Dynamics;2023-06-12

3. Dynamic data reconciliation to enhance the performance of model free adaptive control;Measurement Science and Technology;2023-03-03

4. Design of fractional order PID controller based on minimum variance control and application of dynamic data reconciliation for improving control performance;ISA Transactions;2023-02

5. Dynamic Data Reconciliation for Improving the Model Prediction Results based on Just-in-time Kernel Learning;2022 China Automation Congress (CAC);2022-11-25

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