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数据驱动的溶解氧浓度在线自组织控制方法

权利敏 杨翠丽 乔俊飞

权利敏, 杨翠丽, 乔俊飞. 数据驱动的溶解氧浓度在线自组织控制方法. 自动化学报, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c210041
引用本文: 权利敏, 杨翠丽, 乔俊飞. 数据驱动的溶解氧浓度在线自组织控制方法. 自动化学报, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c210041
Quan Li-Min, Yang Cui-Li, Qiao Jun-Fei. Data-driven online self-organizing control for dissolved oxygen concentration. Acta Automatica Sinica, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c210041
Citation: Quan Li-Min, Yang Cui-Li, Qiao Jun-Fei. Data-driven online self-organizing control for dissolved oxygen concentration. Acta Automatica Sinica, 2021, 47(x): 1−12 doi: 10.16383/j.aas.c210041

数据驱动的溶解氧浓度在线自组织控制方法

doi: 10.16383/j.aas.c210041
基金项目: 国家自然科学基金(62021003, 61890930-5, 61973010), 北京市自然科学基金资助项目(4202006), 水体污染控制与治理科技重大专项项目(2018ZX07111005)资助
详细信息
    作者简介:

    权利敏:北京工业大学信息学部博士研究生. 主要研究方向为数据驱动建模与控制, 城市污水处理过程智能控制, 神经网络结构优化设计. E-mail: quanlimin12@sina.com

    杨翠丽:北京工业大学信息学部副教授. 主要研究方向为神经网络和智能优化算法. E-mail: clyang5@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控制, 神经网络结构设计与分析. 本文通信作者. E-mail: junfeq@bjut.edu.cn

Data-Driven Online Self-organizing Control for Dissolved Oxygen Concentration

Funds: Supported by National Natural Science Foundation of P. R. China (62021003, 61890930-5, 61973010), Natural Science Foundation of Beijing (4202006), and Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005)
More Information
    Author Bio:

    QUAN Li-Min Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. Her current research interests include data-driven modeling and control, intelligent control of urban wastewater treatment process, and structure design and optimization of neural networks

    YANG Cui-Li Associate professor at Faculty of Information Technology, Beijing University of Technology. Her research interest covers neural network and intelligent optimization algorithm

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

  • 摘要: 针对城市污水处理过程的非线性, 不确定性以及非高斯等特点, 提出一种基于数据驱动的溶解氧浓度在线自组织控制方法. 首先, 设计了一种基于相关熵的自组织模糊神经网络控制器(Correntropy-based self-organizing fuzzy neural network, CSOFNN), 采用相关熵与规则贡献度指标实现控制器结构与参数的自动构建或修剪. 其次, 设计了基于相关熵诱导准则的补偿控制器及参数自适应律, 充分利用了相关熵抑制非高斯噪声的能力, 能够有效地降低系统中的不确定性. 然后, 分析了所提出的控制方法的稳定性, 从而保证其在实际应用中的可靠性. 最后, 基于基准仿真1号模型的实验验证了所提方法的有效性.
  • 图  1  A/O工艺城市污水处理流程图

    Fig.  1  Schematic diagram of urban WWTP with the A/O process

    图  2  基于CSOFNN的溶解氧浓度控制系统框图

    Fig.  2  Diagram of the CSOFNN-based control system for the DO concentration

    图  3  外部环境干扰

    Fig.  3  External disturbance

    图  4  CSOFNN控制器规则变化曲线

    Fig.  4  Rules variations of CSOFNN controller

    图  5  DO浓度跟踪控制效果

    Fig.  5  Control performance of SO

    图  6  DO浓度跟踪控制误差

    Fig.  6  Control errors of SO

    图  7  阴雨工况KLa5变化曲线

    Fig.  7  Variations of KLa5 in rain weather

    图  8  CSOFNN控制器规则变化曲线

    Fig.  8  Rules variations of CSOFNN controller

    图  9  DO浓度跟踪控制效果

    Fig.  9  Control performance of SO

    图  10  DO浓度跟踪控制误差

    Fig.  10  Control errors of SO

    表  1  不同控制器的性能比较

    Table  1  Performance comparisons of different controllers

    干扰类型控制器No.IAEISEDevmax
    连续降雨CSOFNN42.1×10−33.00×10−67.44×10−3
    CFNN[18]6*3.2×10−3*8.76×10−6*7.56×10−3*
    SOFC[14]10*3.1×10−2*7.26×10−4*3.6×10−2*
    SOFNN[20]12*4.2×10−2*1.81×10−4*1.12×10−2*
    突发暴雨CSOFNN41.9×10−31.44×10−63.42×10−3
    CFNN[18]6*2.1×10−3*1.75×10−6*3.46×10−3*
    SOFC[14]9*2.5×10−2*8.63×10−4*9.7×10−2*
    SOFNN[20]12*6.0×10−2*1.19×10−3*8.22×10−2*
    *表示原文中的结果, 粗体表示最好的结果
    下载: 导出CSV

    表  2  不同控制器的性能比较

    Table  2  Performance comparisons with different control strategies

    干扰类型控制器规则数IAEISEDevmax
    降雨CSOFNN52.4×10−31.67×10−47.14×10−3
    CFNN[18]6*2.1×10−3*2.34×10−4*7.60×10−3*
    SOFC[14]14*2.2×10−2*2.86×10−4*3.5×10−2*
    SOTSFNN[19]9*0.48*9.7×10−4*1.0×10−2*
    降雨+脉冲噪声CSOFNN64.8×10−33.41×10−42.02×10−2
    CFNN[18]63.63×10−21.11×10−33.22×10−2
    SOFC[14]104.49×10−29.97×10−43.62×10−2
    SOTSFNN[19]201.332.47×10−24.29×10−2
    *表示原文中的结果, 粗体表示最好的结果
    下载: 导出CSV
  • [1] Deletic A, Wang H. Water pollution control for sustainable development. Engineering, 2019, 5(05): 62−65
    [2] 杨翠丽, 武战红, 韩红桂, 乔俊飞. 城市污水处理过程优化设定方法研究进展. 自动化学报, 2020, 46(10): 2092−2108

    Yang Cui-Li, Wu Zhan-Hong, Han Hong-Gui, Qiao Jun-Fei. Perspectives on optimal setting methods for municipal wastewater treatment processes. Acta Automatica Sinica, 2020, 46(10): 2092−2108
    [3] 乔俊飞, 韩改堂, 周红标. 基于知识的污水生化处理过程智能优化方法. 自动化学报, 2017, 43(6): 1038−1046

    Qiao Jun-Fei, Han Gai-Tang, Zhou Hong-Biao. Knowledge-based intelligent optimal control for wastewater biochemical treatment process. Acta Automatica Sinica, 2017, 43(6): 1038−1046
    [4] 魏伟, 陈楠, 左敏, 刘载文. 基于复合抗扰的溶解氧浓度控制. 控制理论与应用, 2020, 37(09): 1895−1903

    Wei Wei, Chen Nan, Zuo Min, Liu Zai-Wen. Compound disturbance rejection control of dissolved oxygen concentration. Control Theory & Applications, 2020, 37(09): 1895−1903
    [5] 蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量. 自动化学报, 2019, 45(5): 906−919

    Meng Xi, Qiao Jun-Fei, Han Hong-Gui. Soft measurement of key effluent parameters in wastewater treatment process using brain-like modular neural networks. Acta Automatica Sinica, 2019, 45(5): 906−919
    [6] Santín I, Barbu M, Pedret C, Vilanova R. Dissolved oxygen control in biological wastewater treatments with non-ideal sensors and actuators. Industrial & Engineering Chemistry Research, 2019, 58(45): 20639−20654
    [7] van Waarde H J, Eising J, Trentelman H L, Camlibel M K. Data informativity: a new perspective on data-driven analysis and control. IEEE Transactions on Automatic Control, 2020, 65(11): 4753−4768 doi: 10.1109/TAC.2020.2966717
    [8] Ding Y S, Xu N, Ren L H, and Hao K R. Data-driven neuroendocrine ultrashort feedback-based cooperative control system. IEEE Transactions on Control Systems Technology, 2015, 23: 1205−1212 doi: 10.1109/TCST.2014.2359386
    [9] Belchior, C A C, Araújo R A M, Landeck J A C. Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control. Computers & Chemical Engineering, 2012, 37: 152−162
    [10] 张伟, 乔俊飞, 李凡军. 溶解氧浓度的直接自适应动态神经网络控制方法. 控制理论与应用, 2015, 32(1): 115−121 doi: 10.7641/CTA.2014.40311

    Zhang Wei, Qiao Jun-Fei, Li Fun-Jun. Direct adaptive dynamic neural network control for dissolved oxygen concentration. Control Theory & Applications, 2015, 32(1): 115−121 doi: 10.7641/CTA.2014.40311
    [11] Wang D, Ha M M, Qiao J F. Data-driven iterative adaptive critic control towards an urban wastewater treatment plant. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7362−7369 doi: 10.1109/TIE.2020.3001840
    [12] Ruan J, Zhang C, Li Y, Yang Z, Chen X, Huang M, Zhang T. Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor. Journal of Environmental Management, 2017, 187: 550−559
    [13] Eltag K, Aslamx M S, Ullah R. Dynamic stability enhancement using fuzzy PID control technology for power system. International Journal of Control, Automation and Systems, 2019, 17: 234−242 doi: 10.1007/s12555-018-0109-7
    [14] 张帅, 周平. 污水处理过程递推双线性子空间建模及无模型自适应控制. 自动化学报, DOI: 10.16383/j.aas.c190514.

    Zhang Shuai, Zhou Ping. Recursive bilinear subspace modeling and model-free adaptive control of wastewater treatment. Acta Automatica Sinica, DOI: 10.16383/j.aas.c190514.
    [15] Qiao J F, Zhang W, Han H G. Self-organizing fuzzy control for dissolved oxygen concentration using fuzzy neural network. Journal of Intelligent & Fuzzy Systems, 2016, 30(6): 3411−3422
    [16] 韩广, 乔俊飞, 薄迎春. 溶解氧浓度的前馈神经网络建模控制方法. 控制理论与应用, 2013, 30(05): 585−591

    Han Huang, Qiao Jun-Fei, Bo Ying-Chun. Feedforward neural network modeling and control for dissolved oxygen concentration. Control Theory & Applications, 2013, 30(05): 585−591
    [17] Lin M J, Luo F. An adaptive control method for the dissolved oxygen concentration in wastewater treatment plants. Neural Computing and Applications, 2015, 26(8): 2027−2037 doi: 10.1007/s00521-015-1858-3
    [18] Han H G, Zhang L, Liu H X, et al. Multiobjective design of fuzzy neural network controller for wastewater treatment process. Applied Soft Computing, 2018, 67: 467−478 doi: 10.1016/j.asoc.2018.03.020
    [19] 权利敏, 杨翠丽, 乔俊飞. 基于CFNN的污水处理过程溶解氧浓度在线控制. 智能科学与技术学报, 2020, 2(03): 261−267

    Quan Li-Min, Yang Cui-Li, Qiao Jun-Fei. CFNN-based online control for dissolved oxygen concentration of wastewater treatment processes. Chinese Journal of Intelligent Science and Technologie, 2020, 2(03): 261−267
    [20] 乔俊飞, 付文韬, 韩红桂. 基于SOTSFNN的溶解氧浓度控制方法. 化工学报, 2016, 67(03): 960−966

    Qiao Jun-Fei, Fu Wen-Tao, Han Hong-Gui. Dissolved oxygen control method based on self-organizing T-S fuzzy neural network. CIESC Journal, 2016, 67(03): 960−966
    [21] 周红标. 基于自组织模糊神经网络的污水处理过程溶解氧控制. 化工学报, 2017, 68(04): 1516−1524

    Zhou Hong-Biao. Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network. CIESC Journal, 2017, 68(04): 1516−1524
    [22] Chen B, Liu X, Zhao H, Principe J C. Maximum correntropy Kalman filter. Automatica, 2017, 76: 70−77 doi: 10.1016/j.automatica.2016.10.004
    [23] Yu, J. A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes. Chemical Engineering Science, 2012, 68: 506−519 doi: 10.1016/j.ces.2011.10.011
    [24] Liu W, Pokharel P P, Principe J C. Correntropy: Properties and applications in non-Gaussian signal processing. IEEE Transactions on Signal Processing, 2007, 55(11): 5286−5298 doi: 10.1109/TSP.2007.896065
    [25] Bao R-J, Rong H-J, Angelov P P, et al. Correntropy-based evolving fuzzy neural system. IEEE Transactions on Fuzzy Systems, 2018, 26: 1324−1338 doi: 10.1109/TFUZZ.2017.2719619
    [26] Cao J, Dai H, Lei B, Yin C, Zeng H, Kummert A. Maximum correntropy criterion-based hierarchical one-class classification. IEEE Transactions on Neural Networks and Learning Systems, 2020 doi: 10.1109/TNNLS.2020.3015356
    [27] Nayyeri M, Yazdi HS, Maskooki A, Rouhani M. Universal approximation by using the correntropy objective function. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 4515−4521 doi: 10.1109/TNNLS.2017.2753725
    [28] Ren M, Gong M, Lin M, Zhang J. Generalized correntropy predictive control for waste heat recovery systems based on organic rankine cycle. IEEE Access, 2019, 7: 151587−151594 doi: 10.1109/ACCESS.2019.2948284
    [29] Jeppsson U, Pons M N. The COST benchmark simulation model current state and future perspective. Control Engineering Practice, 2004, 12(3): 299−304 doi: 10.1016/j.conengprac.2003.07.001
    [30] Chen C S. Robust self-organizing neural-fuzzy control with uncertainty observer for MIMO nonlinear systems. IEEE Transactions on Fuzzy Systems, 2011, 19(4): 694−706 doi: 10.1109/TFUZZ.2011.2136349
    [31] Sun M A. Barbalat-like lemma with its application to learning control. IEEE Transactions on Automatic Control, 2009, 54: 2222−2225 doi: 10.1109/TAC.2009.2026849
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出版历程
  • 收稿日期:  2021-01-13
  • 录用日期:  2021-03-12
  • 网络出版日期:  2021-06-21

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