Neural Network-based Improved Active Disturbance Rejection Control Strategy for Superheated Steam Temperature

CHANG Yaohua; WEI Genyuan; ZHAO Shen; CHEN Liang

doi:10.19805/j.cnki.jcspe.2025.240256

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Neural Network-based Improved Active Disturbance Rejection Control Strategy for Superheated Steam Temperature

更新时间：2025-11-28

- Neural Network-based Improved Active Disturbance Rejection Control Strategy for Superheated Steam Temperature
- Chinese Journal of Power Engineering Vol. 45, Issue 6, Pages: 887-894(2025)
- 作者机构：
  
  1. 华北电力大学自动化系,河北,保定,071003
  2. 保定市综合能源系统状态检测与优化调控重点实验室,河北,保定,071003
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240256
  CLC：
- Published Online：16 June 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
常耀华,韦根原,赵深,陈亮. 基于神经网络的过热汽温改进自抗扰控制策略动力工程学报, 2025, 45(6): 887-894 https://doi.

org/10.19805/j.cnki.jcspe.2025.240256
常耀华,韦根原,赵深,陈亮. 基于神经网络的过热汽温改进自抗扰控制策略动力工程学报, 2025, 45(6): 887-894 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240256.

org/10.19805/j.cnki.jcspe.2025.240256 DOI：

摘要

针对过热汽温系统高阶大惯性、多扰动的问题

提出了一种基于BP神经网络的超前补偿模型辅助线性自抗扰控制(BP-LMLADRC) 。在二阶线性自抗扰控制的基础上

在前馈通路和反馈通路分别串联前馈补偿器和超前补偿器

从而解决低阶线性扩张状态观测器前馈信号和反馈信号不同步以及扰动响应滞后的问题;在此基础上

根据闭环系统传递函数表达式

分析系统稳态性能和闭环参数稳定域

推导出参数调节区间

简化参数整定过程

进一步采用BP神经网络得出最优带宽;最后

将所提出的控制策略应用于过热汽温控制系统

并与多种控制器性能进行对比。结果表明:对于过热汽温系统这类高阶大惯性系统

所提出的控制策略在定值跟踪、抗干扰和鲁棒性上具有明显优势。

Abstract

Concerning the problem of high-order large inertia and multiple disturbance in superheated steam temperature system

a linear active disturbance rejection control based on BP neural network assisted by lead compensation model was proposed. Based on second-order linear active disturbance rejection control

a feedforward compensator and a lead compensator were connected in series in the feedforward and feedback paths

respectively

to solve the problems of asynchrony of feedforward and feedback signals and delay of disturbance response in low order linear extended state observers. On this basis

according to the expression of closed-loop system transfer function

the steady-state system performance and the stable domain of closed-loop parameters were analyzed

the parameter adjustment interval was derived

the parameter tuning process was simplified

and BP neural network was further used to obtain the optimal bandwidth. Finally

the proposed control strategy was applied to superheated steam temperature control system and compared the performance with various controllers. Results show that the proposed control strategy has significant advantages in fixed value tracking

disturbance resistance

and robustness for high-order large inertia systems such as superheated steam temperature system.

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references

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