Multi-Variable Direct Self-Organizing Fuzzy Neural Network Control for Wastewater Treatment Process

被引：14

作者：

Zhang, Wei ^{[1
]}

Qiao, Jun-fei ^{[2
]}

机构：

[1] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo 454000, Henan, Peoples R China

[2] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

来源：

ASIAN JOURNAL OF CONTROL | 2020年 / 22卷 / 02期

基金：

美国国家科学基金会;

关键词：

Multi-variable control; neural network; self-organizing structure; wastewater treatment; DISSOLVED-OXYGEN CONCENTRATION; BARRIER LYAPUNOV FUNCTIONS; ADAPTIVE-CONTROL; PREDICTIVE CONTROL; NONLINEAR-SYSTEMS; CASCADE CONTROL; DESIGN; SOFTWARE;

D O I：

10.1002/asjc.1919

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A multi-variable direct self-organizing fuzzy neural network control (M-DSNNC) method is proposed for the multi-variable control of the wastewater treatment process (WWTP). In this paper, the proposed control system is an essential multi-variable control method for the WWTP. No exact plant model is required, which avoids the difficulty of establishing the mathematics model of WWTP. The M-DSNNC system is comprised of a fuzzy neural network controller and a compensation controller. The fuzzy neural network is used for approximating the ideal control law under a general nonlinear system. Moreover, the neural network is designed in a self-organizing mode to adapt the uncertainty environment. Simulation results, based on the international benchmark simulation model No.1 (BSM1), demonstrate that the control accuracy is improved under the proposed M-DSNNC method, and the controller has a much stronger decoupling ability.

引用

页码：716 / 728

页数：13

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