MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration

被引：34
作者：
Ding, Haixu
Tang, Jian
Qiao, Junfei [1 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
来源：
CONTROL ENGINEERING PRACTICE | 2022年 / 127卷
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
Municipal solid wastes incineration; Quasi-diagonal recurrent neural network; Multi-loop PID controller; Multi-input multi-output; Multi-task learning; PID CONTROL; DESIGN;
D O I：
10.1016/j.conengprac.2022.105280
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Municipal solid wastes incineration (MSWI) process with complex mechanism and strong nonlinearity is an important part of resource cycle. It is a challenge to design its multivariable controlled object model and control strategy. To solve this problem, a multi-input multi-output (MIMO) data-driven model and a multi-loop PID controller are proposed in this paper. Firstly, a feature selection method based on Pearson correlation coefficient (PCC) and expert knowledge is used to analyze the relationship between the manipulated variable and the controlled variable of MSWI process. Secondly, a MIMO Takagi-Sugeno fuzzy neural network (TSFNN) based on multi-task learning (MTL) is designed to construct the multivariable controlled object model. Thirdly, a multi-loop PID controller based on quasi-diagonal recurrent neural network (QDRNN) is constructed, which has self-feedback channel and interconnection channel, and can adjust the control parameters automatically. Next, the stability of control strategy is proved by Lyapunov second method. Finally, the modeling effect and control performance are confirmed on the simulation experiments based on the real MSWI process data.
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页数：18
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