Paper Info
Title
Design of Syncretic Fuzzy-Neural Control for WWTP
Abstract
Owing to the possible existence of system failures and packet dropouts in the wastewater treatment process (WWTP), it is difficult to obtain sufficient data, which will result in data shortage. Therefore, it is a challenge to design an effective data-driven controller with the above data shortage issue for WWTP. To solve this problem, a syncretic fuzzy-neural controller (SFNC) was developed and analyzed in this article. First, the knowledge obtained from the operation conditions was made full use by a knowledge reconstruction mechanism to construct the initial condition of SFNC. Then, the proposed SFNC was able to obtain the accurate parameters and compact structure in the initialization phase. Second, a syncretic-form strategy (SFS) was designed to syncretize the knowledge from the fuzzy rules and the data from the operation process to optimize the structure of SFNC. Then, the adaptability of SFNC can be improved to achieve good control performance in the presence of insufficient data. Third, the stability of the developed SFNC was proved by using Lyapunov stability theorem. Then, the stability of SFNC was given to guarantee its successful application. Finally, the proposed controller was tested on the Benchmark Simulation Model No.1 to confirm its effectiveness. The results demonstrated that the proposed SFNC can achieve superior control performance than some other existing controllers.
Year
DOI
Venue
2022
10.1109/TFUZZ.2021.3075842
IEEE Transactions on Fuzzy Systems
Keywords
DocType
Volume
Knowledge reconstruction mechanism (KRM),stability,syncretic fuzzy-neural controller (SFNC),syncretic-form strategy (SFS),wastewater treatment process (WWTP)
Journal
30
Issue
ISSN
Citations 
8
1063-6706
0
PageRank 
References 
Authors
0.34
31
4
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
zheng liu226721.86
Jiaming Li300.34
Jun-Fei Qiao479874.56