自动化学报2025，Vol.51Issue(12)：2679-2690,12.DOI:10.16383/j.aas.c250359
城市污水处理进水过程自适应恒压控制
Adaptive Constant Pressure Control for the Influent Process of Municipal Wastewater Treatment
摘要
Abstract
To address the problem that the main pipe pressure of the influent process in municipal wastewater treatment is difficult to be stably controlled,which leads to the unstable operation of the wastewater treatment pro-cess,an adaptive constant pressure control(ACPC)method is proposed.Firstly,a control model for the influent process of municipal wastewater treatment is established based on time-delay characteristics.A Pade approximation-based equivalent transformation model is designed to dynamically compensate for the influence caused by time-delay in the operation process.Secondly,a barrier Lyapunov function-based ACPC method is proposed,and a transfer strategy is designed to relieve the constraint imposed by the initial main pipe pressure on the controller.This ensures that the influent process operates within the specified pressure range.Then,a neural network-based adaptive dynamic adjustment algorithm is proposed to adjust control parameters in real time according to the oper-ation status,reducing the main pipe pressure fluctuations caused by cleaning operations of the membrane module and improving the stability of the influent process.Finally,the stability analysis of the controller is provided,prov-ing that all variables in the influent process are semi-globally ultimately bounded.This confirms the realization of stable control over the main pipe pressure.Simulation tests and practical verification results show that this method can ensure the stable operation of the influent process in municipal wastewater treatment.关键词
城市污水处理进水过程/帕德逼近方法/障碍李雅普诺夫函数/自适应恒压控制Key words
Influent process of municipal wastewater treatment/Pade approximation method/barrier Lyapunov function/adaptive constant pressure control引用本文复制引用
HAN Hong-Gui,JI Wei-Yu,LIU Zheng,SUN Hao-Yuan,WU Xiao-Long..城市污水处理进水过程自适应恒压控制[J].自动化学报,2025,51(12):2679-2690,12.基金项目
国家自然科学基金(62125301,62021003,62103010,62303024,62473011,92467205,U24A20275),青年北京学者项目(037),北京市科技新星计划(20240484694,20250484938),北京市自然科学基金(L253010,Z250021)资助 Supported by National Natural Science Foundation of China(62125301,62021003,62103010,62303024,62473011,92467205,U24A20275),the Youth Beijing Scholars Program(037),Beijing Nova Program(20240484694,20250484938),and Beijing Natural Science Foundation(L253010,Z250021) （62125301,62021003,62103010,62303024,62473011,92467205,U24A20275）
