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首页> 外文期刊>International Journal of Fuzzy Systems >Fuzzy Neural Network-Based Model Predictive Control for Dissolved Oxygen Concentration of WWTPs
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Fuzzy Neural Network-Based Model Predictive Control for Dissolved Oxygen Concentration of WWTPs

机译:基于模糊神经网络的污水处理厂溶解氧浓度模型预测控制

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摘要

Dissolved oxygen (DO) concentration is a key variable in the operation of wastewater treatment processes (WWTPs). In this paper, an adaptive fuzzy neural network-based model predictive control (AFNN-MPC) is proposed for the control problem of DO concentration. The main contributions of AFNN-MPC are threefolds: First, an AFNN, based on a novel learning method with adaptive learning rate, is designed to model the unknown nonlinearities of WWTPs with high predicting performance. Second, a gradient method is used to solve the optimal control problem of AFNN-MPC to reduce the computational cost. Third, the convergence of AFNN, as well as the stability analysis of AFNN-MPC, has been given in detail. Finally, the proposed AFNN-MPC is applied to the benchmark simulation model No. 2. The promising results indicate that the proposed AFNN-MPC is a suitable solution to control DO concentration. Moreover, the comprehensive experiments clearly show the superiority and efficacy of the proposed AFNN-MPC.
机译:溶解氧(DO)浓度是废水处理过程(WWTP)运行中的关键变量。针对溶解氧浓度的控制问题,提出了一种基于自适应模糊神经网络的模型预测控制(AFNN-MPC)。 AFNN-MPC的主要贡献有三个方面:首先,基于具有自适应学习率的新型学习方法,AFNN被设计为具有较高预测性能的WWTP未知非线性模型。其次,采用梯度法解决了AFNN-MPC的最优控制问题,降低了计算量。第三,详细介绍了AFNN的收敛性以及AFNN-MPC的稳定性。最后,将提出的AFNN-MPC应用于2号基准仿真模型。有希望的结果表明,提出的AFNN-MPC是控制DO浓度的合适解决方案。此外,综合实验清楚地表明了所提出的AFNN-MPC的优越性和有效性。

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