Paper Info
Title
Knowledge-Data-Driven Flexible Switching Control for Wastewater Treatment Process
Abstract
The wastewater treatment process (WWTP), including multiple operation conditions, is a complex industrial process with strong nonlinearity and time-varying dynamics. It is a challenge to design an effective controller for this kind of process. To solve this problem, a knowledge-data-driven flexible switching controller is designed and analyzed to achieve reliable control performance. First, a flexible switching control strategy is proposed to build multiple operation models to approximate different operation conditions. Then, multiple subcontrollers are designed for the multiple operation models to suppress the nonlinearity and time-varying dynamics of WWTP. Second, a knowledge-data-driven framework, based on data sharing and knowledge-driven mechanisms, is developed to learn the subcontrollers. Then, the internal data and external knowledge can be fully leveraged to improve the control accuracy. Third, the stability of the proposed control strategy is given in detail. The corresponding stability conditions are provided to guide its application. Finally, the control performance is confirmed on the benchmark simulation model No. 1. The results demonstrate that the proposed KDFSC can achieve excellent control performance.
Year
DOI
Venue
2022
10.1109/TCST.2021.3095849
IEEE Transactions on Control Systems Technology
Keywords
DocType
Volume
Data sharing mechanisms,interactive transfer learning mechanism,knowledge-based switching controller,multiple operation models,wastewater treatment process (WWTP)
Journal
30
Issue
ISSN
Citations 
3
1063-6536
0
PageRank 
References 
Authors
0.34
28
3
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Hong-Xu Liu292.81
Jun-Fei Qiao379874.56