Wastewater treatment control method based on adaptive recurrent fuzzy neural network

被引：3

作者：

Han G.-T. ^{[1
,2
]}

Qiao J.-F. ^{[1
,2
]}

Han H.-G. ^{[1
,2
]}

机构：

[1] College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing

[2] Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2016年 / 33卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Adaptive learning rate; Benchmark simulation model 1 (BSM1); Recurrent fuzzy neural network; Wastewater treatment;

D O I：

10.7641/CTA.2016.50965

中图分类号：

学科分类号：

摘要：

Due to the nonlinear and highly time-varying issues of wastewater treatment processes, a wastewater treatment control method based on adaptive recurrent fuzzy neural network (RFNN) is proposed. Firstly, the adaptive RFNN identifier is used to establish the nonlinear dynamic model of wastewater treatment process. The model can afford the state variable information of wastewater treatment process to RFNN controller, which can ensure the accuracy of manipulated variable is adjusted by controller. Secondly, RFNN identifier and RFNN controller are learning through gradient descent algorithm with an adaptive learning rate, which guarantee the convergence of learning process of RFNN, and a function is constructed by lyapunov theory to prove the convergence of this algorithm. Finally, the simulation experiment carried out based on BSM1 platform. Compared with PID, model predictive control and forward neural network control techniques, the simulation results show that the proposed method can improve obviously the control accuracy of wastewater treatment. © 2016, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：1252 / 1258

页数：6

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