2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于ESN的多指标DHP控制策略在污水处理过程中的应用

乔俊飞 薄迎春 韩广

乔俊飞, 薄迎春, 韩广. 基于ESN的多指标DHP控制策略在污水处理过程中的应用. 自动化学报, 2013, 39(7): 1146-1151. doi: 10.3724/SP.J.1004.2013.01146
引用本文: 乔俊飞, 薄迎春, 韩广. 基于ESN的多指标DHP控制策略在污水处理过程中的应用. 自动化学报, 2013, 39(7): 1146-1151. doi: 10.3724/SP.J.1004.2013.01146
QIAO Jun-Fei, BO Ying-Chun, HAN Guang. Application of ESN-based Multi Indices Dual Heuristic Dynamic Programming on Wastewater Treatment Process. ACTA AUTOMATICA SINICA, 2013, 39(7): 1146-1151. doi: 10.3724/SP.J.1004.2013.01146
Citation: QIAO Jun-Fei, BO Ying-Chun, HAN Guang. Application of ESN-based Multi Indices Dual Heuristic Dynamic Programming on Wastewater Treatment Process. ACTA AUTOMATICA SINICA, 2013, 39(7): 1146-1151. doi: 10.3724/SP.J.1004.2013.01146

基于ESN的多指标DHP控制策略在污水处理过程中的应用

doi: 10.3724/SP.J.1004.2013.01146
基金项目: 

国家自然科学基金(61034008),教育部博士点基金(200800050004),北京市自然科学基金(4092010)资助

详细信息
    通讯作者:

    乔俊飞

Application of ESN-based Multi Indices Dual Heuristic Dynamic Programming on Wastewater Treatment Process

Funds: 

Supported by National Natural Science Foundation of China (61034008), Doctoral Fund of Ministry of Education of China (200800050004) and Beijing Municipal Natural Science Foundation (4092010)

  • 摘要: 针对污水处理过程(Wastewater treatment process, WWTP)溶解氧(Dissolved oxygen, DO)及硝态氮浓度控制问题, 提出了一种多评价指标的DHP (Dual heuristic dynamic programming)控制策略. 该策略能够降低评价指标的复杂性, 提高评价网络的逼近精度. 采用回声状态网络(Echo state networks, ESNs)实现评价函数及控制策略的逼近, 研究了控制器的在线学习算法. 实验表明, 该策略在控制性能上优于单评价指标的DHP策略及常规PID控制策略.
  • [1] Shi Xiong-Wei, Qiao Jun-Fei, Yuan Ming-Zhe. Optimal control for wastewater treatment process based on improved particle optimization algorithm. Information and Control, 2011, 40(5): 698-703 (史雄伟, 乔俊飞, 苑明哲. 基于改进粒子群优化算法的污水处理过程优化控制. 信息与控制, 2011, 40(5): 698-703)
    [2] Holenda B, Domokos E, Rédey A, Fazakas J. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control. Computers and Chemical Engineering, 2008, 32(6): 1270-1278
    [3] Dellana S A, West D. Predictive modeling for wastewater applications: linear and nonlinear approaches. Environmental Modelling & Software, 2009, 24(1): 96-106
    [4] Zhang H G, Cui L L, Zhang X, Luo Y H. Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method. IEEE Transactions on Neural Networks, 2011, 22(12): 2226-2236
    [5] Lewis F L, Vamvoudakis K G. Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data. IEEE Transactions on Systems, Man, and Cybernetics — Part B: Cybernetics, 2011, 41(1): 14-25
    [6] Wang F Y, Zhang H G, Liu D R. Adaptive dynamic programming: an introduction. IEEE Computational Intelligence Magazine, 2009, 4(2): 39-47
    [7] Wei Qing-Lai, Zhang Hua-Guang, Cui Li-Li. Data-based optimal control for discrete-time zero-sum games of 2-D systems using adaptive critic designs. Acta Automatica Sinica, 2009, 35(6): 682-692(魏庆来, 张化光, 崔黎黎. 基于数据自适应评判的离散2-D系统零和博弈最优控制. 自动化学报, 2009, 35(6): 682-692)
    [8] Wei Qing-Lai, Zhang Hua-Guang, Liu De-Rong, Zhao Yan. An optimal control scheme for a class of discrete-time nonlinear systems with time delays using adaptive dynamic programming. Acta Automatica Sinica, 2010, 36(1): 121-129 (魏庆来, 张化光, 刘德荣, 赵琰. 基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略. 自动化学报, 2010, 36(1): 121-129)
    [9] Fu J, He H B, Zhou X M. Adaptive learning and control for MIMO system based on adaptive dynamic programming. IEEE Transactions on Neural Networks, 2011, 22(7): 1133-1148
    [10] Zhao Dong-Bin, Liu De-Rong, Yi Jian-Qiang. An overview on the adaptive dynamic programming based urban city traffic signal optimal control. Acta Automatica Sinica, 2009, 35(6): 676-681 (赵冬斌, 刘德荣, 易建强. 基于自适应动态规划的城市交通信号优化控制方法综述. 自动化学报, 2009, 35(6): 676-681)
    [11] White D A, Sofge D A. Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches. New York: Van Nostrand Reinhold Press, 1992
    [12] Jaeger H. The "echo state" approach to analysing and training recurrent neural networks. GMD Report, German National Research Center for Information Technology, 2001, 12(8): 1-43
    [13] Busoniu L, Babuska R, De Schutter B. Reinforcement Learning and Dynamic Programming Using Function Approximators. Boca Raton: CRC Press, 2010
  • 加载中
计量
  • 文章访问数:  1730
  • HTML全文浏览量:  93
  • PDF下载量:  1105
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-12-30
  • 修回日期:  2012-11-29
  • 刊出日期:  2013-07-20

目录

    /

    返回文章
    返回