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城市污水处理过程自适应滑模控制

韩红桂 秦晨辉 孙浩源 乔俊飞

韩红桂, 秦晨辉, 孙浩源, 乔俊飞. 城市污水处理过程自适应滑模控制. 自动化学报, 2023, 49(5): 1010−1018 doi: 10.16383/j.aas.c210798
引用本文: 韩红桂, 秦晨辉, 孙浩源, 乔俊飞. 城市污水处理过程自适应滑模控制. 自动化学报, 2023, 49(5): 1010−1018 doi: 10.16383/j.aas.c210798
Han Hong-Gui, Qin Chen-Hui, Sun Hao-Yuan, Qiao Jun-Fei. Adaptive sliding mode control for municipal wastewater treatment process. Acta Automatica Sinica, 2023, 49(5): 1010−1018 doi: 10.16383/j.aas.c210798
Citation: Han Hong-Gui, Qin Chen-Hui, Sun Hao-Yuan, Qiao Jun-Fei. Adaptive sliding mode control for municipal wastewater treatment process. Acta Automatica Sinica, 2023, 49(5): 1010−1018 doi: 10.16383/j.aas.c210798

城市污水处理过程自适应滑模控制

doi: 10.16383/j.aas.c210798
基金项目: 国家重点研发项目(2022YFB3305800-5), 国家自然科学基金(62125301, 61890930-5, 61903010, 62021003), 北京市卓越青年科学家计划项目(BJJWZYJH01201910005020), 北京市教育委员会科技计划重点项目(KZ202110005009), 青年北京学者项目(037)资助
详细信息
    作者简介:

    韩红桂:北京工业大学信息学部教授. 主要研究方向为城市污水处理过程智能优化控制, 神经网络结构设计与优化. 本文通信作者. E-mail: rechardhan@bjut.edu.cn

    秦晨辉:北京工业大学信息学部硕士研究生. 主要研究方向为城市污水处理过程智能控制. E-mail: qinchenqinchen123@163.com

    孙浩源:北京工业大学信息学部讲师. 主要研究方向为城市污水处理网络化控制, 随机采样控制. E-mail: sunhaoyuan@bjut.edu.cn

    乔俊飞:北京工业大学信息学部教授. 主要研究方向为城市污水处理过程智能优化控制, 神经网络结构设计与优化. E-mail: adqiao@bjut.edu.cn

Adaptive Sliding Mode Control for Municipal Wastewater Treatment Process

Funds: Supported by National Key Research and Development Project (2022YFB3305800-5), National Natural Science Foundation of China (62125301, 61890930-5, 61903010, 62021003), Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), Key Project of Science and Technology Plan of Beijing Municipal Education Commission (KZ202110005009), and Youth Beijing Scholars Program (037)
More Information
    Author Bio:

    HAN Hong-Gui Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimal control of municipal waste-water treatment process, structure design and optimization of neural networks. Corresponding author of this paper

    QIN Chen-Hui Master student at the Faculty of Information Technology, Beijing University of Technology. His main research interest is intelligent control of municipal wastewater treatment process

    SUN Hao-Yuan Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interest covers networked control of municipal wastewater treatment process and stochastic sampled-data control

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimal control of municipal wastewater treatment process, structure design and optimization of neural networks

  • 摘要: 针对城市污水处理过程时滞导致难以稳定控制的问题, 提出一种自适应滑模控制方法(Adaptive sliding mode control, ASMC). 首先, 分析推流时滞对城市污水处理生化反应过程的影响, 建立时滞影响下的城市污水处理运行控制模型; 其次, 设计一种基于模糊神经网络的预估补偿模型, 完成滞后变量的准确预测, 实现控制模型中变量时刻的统一; 最后, 设计一种具有自适应开关增益系数的滑模控制器(Sliding mode control, SMC), 实现溶解氧和硝态氮的稳定控制. 将提出的自适应滑模控制方法应用于城市污水处理过程基准仿真平台, 实验结果显示该方法能够实现城市污水处理运行过程稳定控制.
  • 图  1  自适应滑模控制器结构

    Fig.  1  Schematic diagram of adaptive sliding mode control

    图  2  自适应滑模控制求解过程

    Fig.  2  The calculation process of adaptive sliding mode control

    图  3  溶解氧和硝态氮控制效果 (晴天天气且设定值恒定)

    Fig.  3  The control results of $ {{S_{O,5}}}$ and $ {{S_{NO,2}}}$ (dry weather with constant settings)

    图  4  溶解氧和硝态氮控制误差 (晴天天气且设定值恒定)

    Fig.  4  The error results of $ {{S_{O,5}}}$ and $ {{S_{NO,2}}}$ (dry weather with constant settings)

    图  5  氧传递系数和内回流变化曲线 (晴天天气且设定值恒定)

    Fig.  5  The results of $ {{K_L}{a_5}}$ and $ {{Q_a}}$ (dry weather with constant settings)

    图  6  溶解氧和硝态氮控制效果 (雨天天气且设定值恒定)

    Fig.  6  The control results of ${{S_{O,5}}}$ and ${{S_{NO,2}}}$ (rain weather with constant settings)

    图  7  溶解氧和硝态氮控制误差 (雨天天气且设定值恒定)

    Fig.  7  The error results of ${{S_{O,5}}}$ and ${{S_{NO,2}}}$ (rain weather with constant settings)

    图  8  氧传递系数和内回流变化曲线 (雨天天气且设定值恒定)

    Fig.  8  The results of $ {{K_L}{a_5}}$ and $ {{Q_a}}$ (rain weather with constant settings)

    图  9  溶解氧和硝态氮控制效果 (晴天天气且设定值变化)

    Fig.  9  The control results of ${{S_{O,5}}}$ and ${{S_{NO,2}}}$ (dry weather with changing settings)

    图  10  溶解氧和硝态氮控制误差 (晴天天气且设定值变化)

    Fig.  10  The error results of ${{S_{O,5}}}$ and ${{S_{NO,2}}}$ (dry weather with changing settings)

    图  11  氧传递系数和内回流变化曲线 (晴天天气且设定值变化)

    Fig.  11  The results of $ {{K_L}{a_5}}$ and $ {{Q_a}}$ (dry weather with changing settings)

    表  1  不同控制器性能比较 (雨天天气且设定值恒定)

    Table  1  The comparison results of different controllers (rain weather with constant settings)

    控制策略溶解氧硝态氮
    ISEIAE${\rm{De}}{{\rm{v}}^{\max }}$ISEIAE${\rm{De}}{{\rm{v}}^{\max }}$
    ASMC4.930 ×$10^{-3}$0.0350.4905.270 ×$10^{-3}$0.0460.420
    SMC[20]5.520 ×$10^{-3}$0.0300.6105.620 ×$10^{-3}$0.0460.440
    FNNC[34]1.060 ×$10^{-2}$0.0750.5609.950 ×$10^{-3}$0.0760.560
    PID[12]1.430 ×$10^{-2}$0.0720.7408.100 ×$10^{-3}$0.0560.530
    下载: 导出CSV

    表  2  不同控制器性能比较 (晴天天气且设定值变化)

    Table  2  The comparison results of different controllers (dry weather with changing settings)

    控制策略溶解氧硝态氮
    ISEIAE${\rm{De}}{{\rm{v}}^{\max }}$ISEIAE${\rm{De}}{{\rm{v}}^{\max }}$
    ASMC2.120 ×$10^{-3}$0.0300.3403.310 ×$10^{-3}$0.0430.330
    SMC[20]3.640 ×$10^{-3}$0.0320.3603.870 ×$10^{-3}$0.0440.390
    FNNC[34]7.750 ×$10^{-3}$0.0610.4809.900 ×$10^{-3}$0.0750.500
    PID[12]7.930 ×$10^{-3}$0.0450.8504.780 ×$10^{-3}$0.0470.490
    下载: 导出CSV
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  • 收稿日期:  2021-08-24
  • 录用日期:  2022-03-01
  • 网络出版日期:  2022-05-08
  • 刊出日期:  2023-05-20

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