信息与控制2025，Vol.54Issue(4)：607-618,12.DOI:10.13976/j.cnki.xk.2024.1842
垃圾协同处置下基于ELM的MISO Hammerstein-Wiener分解炉温度预测控制
ELM Based Temperature Prediction Control for MISO Hammerstein-Wiener Decomposition Furnace under Collaborative Garbage Disposal
摘要
Abstract
To address the limitation that traditional linear models are insufficient to describe the complex system of the decomposition furnace,in collaborative garbage disposal,we propose a multiple-in-put single-output(MISO)Hammerstein-Wiener model for decomposition furnace temperature mod-eling and predictive control based on an extreme learning machine(ELM).This approach aims to achieve stable temperature control of the decomposition furnace.The model uses coal feeding rate and refuse-derived fuel(RDF)as inputs,decomposition furnace temperature as the output,and applies ELM to capturing nonlinear relationships while employing an autoregressive moving average with exogenous input(ARMAX)model to describe dynamic linear relationships.We identify the mixed parameters of the model using the recursive least squares method and estimate the model pa-rameters through singular value decomposition.The control method for the decomposition furnace follows a two-step predictive control approach.First,we establish a nonlinear inverse model.Then,we map these intermediate variables through a nonlinear process to determine the control variables of the model.Simulation experiments demonstrate that incorporating ELM improves the fitting accuracy of the model.The proposed approach exhibits greater stability and improved track-ing performance compared to traditional predictive control methods.关键词
垃圾协同处置/分解炉温度/Hammerstein-Wiener模型/两步法预测控制/极限学习机Key words
collaborative garbage disposal/decomposition furnace tem-perature/Hammerstein-Wiener model/two-step predictive control/extreme learning machine分类
信息技术与安全科学引用本文复制引用
李鑫,刘海军,陈薇,康志伟,解俊哲,赵军,褚彪..垃圾协同处置下基于ELM的MISO Hammerstein-Wiener分解炉温度预测控制[J].信息与控制,2025,54(4):607-618,12.基金项目
安徽省重点研发计划项目(202104a05020054) （202104a05020054）
