基于动态变增益卡尔曼滤波的SCR无偏模型预测控制

陈彦良; 李益国

doi:10.19805/j.cnki.jcspe.2026.240687

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于动态变增益卡尔曼滤波的SCR无偏模型预测控制

数字化与智能化 | 更新时间：2026-10-10

- 基于动态变增益卡尔曼滤波的SCR无偏模型预测控制
- Offset-free Model Predictive Control for SCR System Based on Dynamic Variable Gain Kalman Filter
- 动力工程学报 2026年46卷第1期页码：131-137
- 作者机构：
  
  东南大学能源与环境学院,江苏,南京,210096
- 作者简介：
  
  陈彦良(2000—),男,云南玉溪人,硕士研究生,研究方向为热工控制
  李益国(通信作者),男,教授,博士,E-mail:lyg@seu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(51476027)
- DOI：10.19805/j.cnki.jcspe.2026.240687
  中图分类号：
- 网络首发：2026-01-15，
  
  纸质出版：2026-01-15
- 稿件说明：
移动端阅览
陈彦良,李益国. 基于动态变增益卡尔曼滤波的SCR无偏模型预测控制动力工程学报, 2026, 46(1): 131-137 https://doi.

org/10.19805/j.cnki.jcspe.2026.240687
陈彦良,李益国. 基于动态变增益卡尔曼滤波的SCR无偏模型预测控制动力工程学报, 2026, 46(1): 131-137 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240687.

org/10.19805/j.cnki.jcspe.2026.240687 DOI：

摘要

为了提高选择性催化还原(SCR)脱硝系统抑制扰动的能力

基于Fal函数提出了一种可以根据新息动态调节卡尔曼滤波器(fKF)增益的方法

然后基于fKF设计了无偏模型预测控制器(fKF-MPC)

并证明fKF-MPC在稳态下可以实现对设定值的无偏跟踪。仿真结果表明:针对SCR这类大惯性系统

fKF-MPC能够更快速而准确地估计扰动

使控制动作更及时

特别是面对快速和随机变化的扰动时

在标称模型以及模型失配的情况下

最大绝对误差(

max

)、平均绝对误差(

)和抑制扰动所需的时间(

)均明显减小

验证了所提方法在抑制扰动方面的优越性。

Abstract

To enhance the disturbance rejection capability of the selective catalytic reduction (SCR) denitrification system

a method for dynamically adjusting the Kalman filter (fKF) gain with a Fal function according to new information was proposed. Then

a offset-free model predictive controller (fKF-MPC) was designed based on the fKF

and it was proven that the fKF-MPC can achieve offset-free tracking of the setpoint at steady state. Simulation results demonstrate that for large-inertia systems like SCR

the fKF-MPC can estimate disturbances more rapidly and accurately

leading to more timely control actions. Particularly when facing fast and randomly varying disturbances

under both nominal model and model mismatch conditions

the maximum absolute error (

max

)

mean absolute error (

)

and the time required for disturbance rejection (

) are all significantly reduced. This validates the superiority of the proposed method in terms of disturbance rejection.

关键词

Keywords

references

曾鹏, 曾喆昭, 黄利容. SCR脱硝系统的串级自耦PID控制方法[J]. 动力工程学报, 2024, 44(1): 84-90. ZENG Peng, ZENG Zhezhao, HUANG Lirong. Cascaded self-coupling PID control of SCR denitration system[J]. Journal of Chinese Society of Power Engineering, 2024, 44(1): 84-90.

杨超杰, 刘长良, 王梓齐, 等. SCR脱硝系统的改进线性自抗扰优化控制研究[J]. 动力工程学报, 2023, 43(7): 893-900.YANG Chaojie, LIU Changliang, WANG Ziqi, et al. Research on optimal control of improved linear active disturbance rejection for SCR denitrification system[J]. Journal of Chinese Society of Power Engineering, 2023, 43(7): 893-900.

杜鸣, 牛玉广, 潘翔峰, 等. 基于改进型模糊线性自抗扰控制器的SCR脱硝系统优化控制[J]. 动力工程学报, 2021, 41(9): 743-748, 757.DU Ming, NIU Yuguang, PAN Xiangfeng, et al. Optimal control of SCR denitrification system based on improved fuzzy linear active disturbance rejection controller[J]. Journal of Chinese Society of Power Engineering, 2021, 41(9): 743-748, 757.

马增辉, 徐慧仪, 朱润潮. 基于互信息和PID神经网络的SCR脱硝扰动补偿控制[J]. 热能动力工程, 2020, 35(5): 281-288.MA Zenghui, XU Huiyi, ZHU Runchao. Disturbance compensation control of SCR denitration based on mutual information and PID neural network[J]. Journal of Engineering for Thermal Energy and Power, 2020, 35(5): 281-288.

陈皓炜, 贾新春, 孙小明, 等. SCR脱硝系统的强化学习复合串级控制[J]. 动力工程学报, 2022, 42(5): 421-428. CHEN Haowei, JIA Xinchun, SUN Xiaoming, et al. Composite cascade control based on reinforcement learning for a SCR denitration system[J]. Journal of Chinese Society of Power Engineering, 2022, 42(5): 421-428.

侯鹏飞, 贾新春, 白建云, 等. SCR烟气脱硝系统多模切换DMC-PID串级预测控制[J]. 控制工程, 2020, 27(2): 355-360.HOU Pengfei, JIA Xinchun, BAI Jianyun, et al. Multi-model switching DMC-PID cascade predictive control for SCR flue gas denitrification system[J]. Control Engineering of China, 2020, 27(2): 355-360.

杨婷婷, 白杨, 吕游, 等. SCR脱硝系统多目标优化控制研究[J]. 中国电机工程学报, 2021, 41(14): 4905-4911.YANG Tingting, BAI Yang, Lü You, et al. Study on multi-objective optimal control of SCR denitrification system[J]. Proceedings of the CSEE, 2021, 41(14): 4905-4911.

ZENG Lingchao, LI Yiguo, LIAO Peizhi, et al. Adaptive disturbance rejection model predictive control and its application in a selective catalytic reduction denitrification system[J]. Computers & Chemical Engineering, 2020, 140: 106963.

XUE Wenchao, MADONSKI R, LAKOMY K, et al. Add-on module of active disturbance rejection for set-point tracking of motion control systems[J]. IEEE Transactions on Industry Applications, 2017, 53(4): 4028-4040.

ZHAO Zhiliang, GUO Baozhu. On active disturbance rejection control for nonlinear systems using time-varying gain[J]. European Journal of Control, 2015, 23: 62-70.

TANG Guojie, XUE Wenchao, HUANG Xiwen, et al. On error-driven nonlinear ESO based control design with application to air—fuel ratio control of engines[J]. Control Engineering Practice, 2024, 143: 105808.

ZHANG Jinhui, CUI Congfeng, GU Shaomeng, et al. Trajectory tracking control of pneumatic servo system: a variable gain ADRC approach[J]. IEEE Transactions on Cybernetics, 2023, 53(11): 6977-6986.

HAN Jingqing. From PID to active disturbance rejection control[J]. IEEE Transactions on Industrial Electronics, 2009, 56(3): 900-906.

王浩坤, 徐祖华, 赵均, 等. 无偏模型预测控制综述[J]. 自动化学报, 2020, 46(5): 858-877.WANG Haokun, XU Zuhua, ZHAO Jun, et al. A survey on offset-free model predictive control[J]. Acta Automatica Sinica, 2020, 46(5): 858-877.

MAEDER U, BORRELLI F, MORARI M. Linear offset-free model predictive control[J]. Automatica, 2009, 45(10): 2214-2222.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于扰动主动利用的孤岛微电网双模模型预测负荷频率控制

基于自适应鲁棒Kalman滤波的吸热器出口熔盐温度预测控制方法

基于模型预测控制的混合储能平抑风电波动控制策略

基于Leckner模型的氨掺烧高水蒸气浓度烟气对燃煤锅炉传热特性的影响分析

基于T-S模糊模型的风力机系统事件触发控制