期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 17, 期 9, 页码 5971-5981出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.3034335
关键词
Process control; Wastewater treatment; Fuzzy control; Stability criteria; Wastewater; Fuzzy neural networks; Cooperative fuzzy-neural control; parameter cooperative strategy; stability analysis; structure cooperative strategy; wastewater treatment process
类别
资金
- National Key Research and Development Project [2018YFC1900800-5]
- National Science Foundation of China [62021003, 61890930-5, 61622301, 61903010]
- Beijing Outstanding Young Scientist Program [BJJWZYJH01201910005020]
In this article, a cooperative fuzzy-neural controller is proposed for wastewater treatment process, which improves operation performance by coordinating structure and parameters. The controller demonstrates superior control precision and low computational burden through a balanced redundant structure and optimized global and local parameters coordination.
Wastewater treatment process, including multiple biochemical reactions, 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 cooperative fuzzy-neural controller is proposed to improve the operation performance of wastewater treatment process in this article. The main advantages of cooperative fuzzy-neural controller contain the following three parts: first, a structure cooperative strategy is developed to adjust the number of fuzzy rules in the controller by coordinating the indexes of similarity and independent contributions. Then, the structure of cooperative fuzzy-neural controller with the balanced redundant degree and efficiency can be adapted to satisfy the different operation conditions of wastewater treatment process. Second, a parameter cooperative strategy is proposed to coordinate the global and local parameters of controller. Then, the parameters can be optimized together to meet the control requirements. Third, the stability of control strategy is given in details. Then, the corresponding stability conditions are shown to guide its application. Finally, the control performance is confirmed on the benchmark simulation model and real wastewater treatment process. The results demonstrate that the proposed cooperative fuzzy-neural controller can achieve superior control precision and low computational burden.
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