智能科学与技术学报 ›› 2020, Vol. 2 ›› Issue (3): 261-267.doi: 10.11959/j.issn.2096-6652.202028

• 学术论文 • 上一篇    下一篇

基于CFNN的污水处理过程溶解氧浓度在线控制

权利敏1,2,杨翠丽1,2,乔俊飞1,2()   

  1. 1 北京工业大学信息学部，北京 100124
    2 计算智能与智能系统北京市重点实验室，北京 100124
  • 修回日期:2020-08-19 出版日期:2020-09-20 发布日期:2020-10-20
  • 作者简介:权利敏（1989- ），女，北京工业大学信息学部博士生，主要研究方向为污水处理过程的建模与智能控制|杨翠丽（1986-），女，博士，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|乔俊飞（1968- ），男，博士，北京工业大学信息学部教授、博士生导师，国家杰出青年科学基金获得者，教育部新世纪优秀人才，主要研究方向为智能信息处理、智能控制理论与应用
  • 基金资助:
    国家自然科学基金资助项目(61533002);国家自然科学基金资助项目(61890930);国家自然科学基金资助项目(61973010);国家重点研发计划基金资助项目(2018YFC19008005);水体污染控制与治理科技重大专项基金资助项目(2018ZX07111005)

CFNN-based online control for dissolved oxygen concentration of wastewater treatment processes

Limin QUAN1,2,Cuili YANG1,2,Junfei QIAO1,2()   

  1. 1 Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
    2 Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China
  • Revised:2020-08-19 Online:2020-09-20 Published:2020-10-20
  • Supported by:
    The National Natural Science Foundation of China(61533002);The National Natural Science Foundation of China(61890930);The National Natural Science Foundation of China(61973010);The National Key Research and Development Program of China(2018YFC19008005);The Major Science and Technology Program for Water Pollution Control and Treatment of China(2018ZX07111005)

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摘要：

污水处理过程入水干扰严重，不确定性强，因而对溶解氧浓度进行实时控制与精确控制比较困难。为了提高溶解氧浓度的控制精度和控制器的鲁棒性，提出一种基于相关熵模糊神经网络（CFNN）的溶解氧浓度在线控制方法。首先，建立了基于跟踪误差相关熵的性能准则，以抑制较大的异常值。其次，基于在线梯度下降算法调整控制器的参数，并分析了系统的稳定性。最后，基于基准仿真 1 号模型（BSM1）进行实验。结果表明，提出的CFNN控制器能够实时精准地跟踪溶解氧浓度，相比其他基于均方误差准则的神经网络控制器，其具有更高的控制精度与稳定性。

关键词: 溶解氧, 在线控制, 相关熵, 模糊神经网络, 污水处理过程

Abstract:

Due to the frequent disturbance in flow and load,as well as the large uncertainty in the wastewater treatment processes,it is difficult to control the dissolved oxygen accurately and in real-time.To improve the accuracy and robustness of the controller,an online control method of dissolved oxygen concentration using the correntropy based fuzzy neural network (CFNN) was proposed.First,the performance index was established based on the correntropy of tracking errors to suppress large outliers in the process.Then,the parameters of controller were updated by the online gradient descent algorithm.Moreover,the stability of the control system was analyzed.Finally,the experiments were carried out based on the Benchmark Simulation Model No.1 (BSM1).The results prove that the CFNN controller performs better than the mean square error based neural network controller in accuracy and model stability.

Key words: dissolved oxygen, online control, correntropy, fuzzy neural network, wastewater treatment processes

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