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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

张帅 周平

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, 2022, 48(7): 1747−1759 doi: 10.16383/j.aas.c190514
引用本文: 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, 2022, 48(7): 1747−1759 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, 2022, 48(7): 1747−1759 doi: 10.16383/j.aas.c190514
Citation: Zhang Shuai, Zhou Ping. Recursive bilinear subspace modeling and model-free adaptive control of wastewater treatment. Acta Automatica Sinica, 2022, 48(7): 1747−1759 doi: 10.16383/j.aas.c190514

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

doi: 10.16383/j.aas.c190514
基金项目: 国家自然科学基金(61890934, 61473064, 61790572), 辽宁省“兴辽英才计划” 项目 (XLYC1907132)资助
详细信息
    作者简介:

    张帅:东北大学博士研究生. 2018年获得沈阳工业大学硕士学位. 主要研究方向为无模型自适应控制和预测控制. E-mail: zhangshuaitougao@163.com

    周平:东北大学教授. 分别于2003年、2006年、2013年获得东北大学学士学位、硕士学位和博士学位. 主要研究方向为工业过程运行反馈控制, 数据驱动建模与控制. 本文通信作者. E-mail: zhouping@mail.neu.edu.cn

Recursive Bilinear Subspace Modeling and Model-free Adaptive Control of Wastewater Treatment

Funds: Supported by National Natural Science Foundation of China (61890934, 61473064, 61790572) and Liaoning Revitalization Talents Program (XLYC1907132)
More Information
    Author Bio:

    ZHANG Shuai Ph.D. candidate at Northeastern University. He received his master degree from Shenyang University of Technology in 2018. His research interest covers model free adaptive control and model predictive control

    ZHOU Ping Professor at Northeastern University. He received his bachelor degree, master degree and Ph.D. degree from Northeastern University in 2003, 2006, and 2013, respectively. His research interest covers operation feedback control of industrial process, data-driven modeling and control. Corresponding author of this paper

  • 摘要: 污水处理过程中, 生化反应硝态氮浓度和溶解氧浓度是决定出水水质好坏的两个最关键变量, 难以采用常规基于模型的方法进行有效控制. 本文基于数据驱动建模与控制技术, 提出一种污水处理过程递推双线性子空间辨识(Recursive bilinear subspace identification, RBLSI)建模和无模型自适应控制方法. 首先, 针对污水处理过程的非线性时变动态特性, 采用最小二乘递推双线性子空间辨识方法建立污水处理生化反应过程具有参数自适应能力的递推双线性模型; 其次, 基于建立的数据驱动模型, 采用基于多参数灵敏度分析(Multi-parameter sensitivity analysis, MPSA)和遗传粒子群优化(Genetic algorithm-particle swarm optimization, GA-PSO)算法的无模型自适应控制(Model-free adaptive control, MFAC)方法对硝态氮和溶解氧浓度进行直接数据驱动控制; 最后, 数据实验及其比较分析表明了所提方法的有效性和优越性.
  • 图  1  污水处理工艺流程图

    Fig.  1  Wastewater treatment process

    图  2  不同算法$S_{\rm{NO},2}$$D_{\rm{O},5}$的模型预测效果

    Fig.  2  Model prediction effects of $S_{\rm{NO},2 }$ and $D_{\rm{O},5}$ with different algorithms

    图  3  不同算法$S_{\rm{NO},2}$$D_{\rm{O},5}$的预测误差PDF曲线

    Fig.  3  Error PDF shapes of $S_{\rm{NO},2 }$ and $D_{\rm{O},5}$ prediction with different algorithms

    图  4  控制系统框图

    Fig.  4  The control flow chart

    图  5  控制器参数累计频率分布曲线

    Fig.  5  Cumulative frequency distribution curve of controller parameters

    图  6  CFDL-MFAC控制器灵敏参数优化收敛过程

    Fig.  6  The optimization convergence process of sensitive parameter based on CFDL-MFAC controller

    图  7  方波扰动抗扰性实验

    Fig.  7  Immunity test with square wave disturbance

    图  8  正弦扰动抗扰性实验

    Fig.  8  Immunity test with sine disturbance

    表  1  不同算法的RMSE比较

    Table  1  Comparison of RMSE based on different algorithms

    算法RMSE $(S_{\rm{NO},2 })$RMSE $(D_{\rm{O},5})$
    RBLSI0.01650.0046
    RLSI0.02110.0085
    下载: 导出CSV

    表  2  CFDL-MFAC控制器参数灵敏度分析结果

    Table  2  Sensitivity analysis results of CFDL-MFAC controller parameters

    序号参数含义DS不灵敏参数固定值
    1$\lambda$输入权重因子0.99810.5
    2$\mu $PJM权重因子0.99850.6
    3$\eta $PJM步长因子0.99960.5
    4$\rho $输入步长因子0.93781.0
    5$\alpha $PJM重置限定参数0.9981.5
    6$b_1 $PJM重置限定参数0.99910.55
    7$b_2 $PJM重置限定参数0.99950.8
    8$\phi _{11}(0)$PJM初值0.5551
    9$\phi _{12}(0)$PJM初值−0.6339
    10$\phi _{21}(0)$PJM初值0.7289
    11$\phi _{22}(0)$PJM初值0.5779
    下载: 导出CSV

    表  3  GA-PSO算法参数

    Table  3  GA-PSO algorithm parameters

    前期 GA 参数后期 PSO 参数
    种群规模$M=40$种群规模$M=40$
    指定遗传代数60最大迭代次数240
    基因重组概率0.7加速系数${c_1} = 4,\;{c_2} = 2$
    变异概率0.25最大惯性
    权重系数
    $W_{\max } =0.9$
    子代选择系数T = 1,
    ${\alpha _T} = 0.5$
    最小惯性
    权重系数
    $W_{\min } =0.1$
    下载: 导出CSV

    表  4  不同算法控制性能对比

    Table  4  Comparison of control performance based on different algorithms

    算法RBL-MPCCFDL-MFAC
    控制量平均求解时间 (s)0.09870.00003528
    方波扰动 RMSE$S_{\rm{NO},2} $0.08650.0297
    $D_{\mathrm{O},5} $0.01580.0107
    正弦扰动 RMSE$S_{\mathrm{NO},2} $0.08590.0249
    $D_{\mathrm{O},5} $0.00990.0089
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-05
  • 录用日期:  2019-10-01
  • 网络出版日期:  2022-06-14
  • 刊出日期:  2022-07-01

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