Fuzzy Neural Network Based Tracking Control of Dissolved Oxgen in WWTP

2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA) Pub Date : 2022-11-04 DOI:10.1109/ISPCE-ASIA57917.2022.9970818

Dingyuan Chen, Cuili Yang, Jun-Li Qiao

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Abstract

Wastewater treatment process (WWTP) is a complex industrial process with strong nonlinear and time-varying dynamic characteristics. Dissolved oxygen (DO) concentration is a main factor limiting the effluent quality. Due to the complex biochemical reactions, designing an effective controller for this kind of process is a huge challenge. To achieve efficacious control under actuator saturation, a self-organizing fuzzy neural network adaptive tracking control method is proposed. Firstly, a structured model of actuator saturation is employed to ensure the prescribed steady-state and transient tracking performance. Secondly, the self-organizing fuzzy neural network is used to identify the unknown dynamics in WWTP. Then, the structure learning algorithm with correlation entropy is used to adjust the structure online. Thirdly, the stability of the control strategy is analyzed and the corresponding stability conditions are given. Finally, the simulation results on benchmark simulation model 1 (BSM 1) verify the effectiveness of the control method.

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基于模糊神经网络的污水处理厂溶解氧跟踪控制

污水处理过程是一个复杂的工业过程，具有很强的非线性和时变动态特性。溶解氧(DO)浓度是制约出水水质的主要因素。由于生物化学反应的复杂性，设计一个有效的控制器是一个巨大的挑战。为了在执行器饱和情况下实现有效控制，提出了一种自组织模糊神经网络自适应跟踪控制方法。首先，采用执行器饱和的结构化模型来保证系统的稳态和瞬态跟踪性能。其次，采用自组织模糊神经网络对污水处理系统中的未知动态进行辨识。然后，采用具有相关熵的结构学习算法对结构进行在线调整。第三，分析了控制策略的稳定性，给出了相应的稳定性条件。最后，在基准仿真模型1 (BSM 1)上的仿真结果验证了该控制方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)

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