Decoupling control for wastewater treatment process based on recurrent fuzzy neural network. Issue 3 (19th June 2018)

• Record Type: Journal Article
• Authors: Qiao, Jun‐Fei; Han, Gai‐Tang; Han, Hong‐Gui; Yang, Cui‐Li; Li, Wei
• Is Part Of: Asian journal of control. Volume 21:Issue 3(2019)
• Page Start: 1270
• Page End: 1280
• Publication Date: 2018-06-19
• Subjects: Wastewater treatment process -- decoupling control -- RFNN -- gradient descent algorithm; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.1844
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: Due to the characteristics of strong coupling and high nonlinearity in the control process, an intelligent decoupling control strategy based on recurrent fuzzy neural network (RFNN) is proposed in this paper to control the wastewater treatment process (WWTP). Firstly, the architecture of the RFNN controller is designed with a mechanism analysis of WWTP. Secondly, a decoupling strategy in combination with a gradient descent search algorithm is used to decouple the control loop of dissolved oxygen (DO ) concentration and nitrate nitrogen (SNO ) concentration. Finally, stability analysis based on a Lyapunov function is investigated. The proposed approach has been applied to the WWTP simulation model. Compared to model predictive control, echo state network‐based HDP (E‐HDP), conventional RFNN, and neural network on‐line modelling and controlling methods, the proposed method has better control performance.