自动化学报2023，Vol.49Issue(12)：2582-2593,12.DOI:10.16383/j.aas.c210041
数据驱动的溶解氧浓度在线自组织控制方法
Data-driven Online Self-organizing Control for Dissolved Oxygen Concentration
摘要
Abstract
To deal with the nonlinearity,uncertainty and non-Gaussianity of urban wastewater treatment pro-cesses,this paper proposes a data-driven online self-organizing control method for dissolved oxygen(DO).First,a correntropy-based self-organizing fuzzy neural network(CSOFNN)controller is designed.For CSOFNN,its struc-ture and parameters can be automatically generated or pruned based on the correntropy and rules-contribution in-dexes.Second,the compensation controller and parameter adaptive laws are developed using the correntropy-in-duced criterion,thus can tackle non-Gaussian noise and reduce the system uncertainty.Third,the stability of the proposed control method is analyzed theoretically,thus ensuring its feasibility in practice.Finally,the proposed control method is tested in the benchmark simulation model No.1(BSM1).The experimental results show its ef-fectiveness.关键词
数据驱动控制/相关熵/在线自组织/模糊神经网络/溶解氧浓度/稳定性分析Key words
Data-driven control/correntropy/online self-organizing/fuzzy neural network(FNN)/dissolved oxygen concentration/stability analysis引用本文复制引用
权利敏,杨翠丽,乔俊飞..数据驱动的溶解氧浓度在线自组织控制方法[J].自动化学报,2023,49(12):2582-2593,12.基金项目
国家自然科学基金(62021003,61890930-5,61973010),科技创新2030——"新一代人工智能"重大项目(2021ZD0112302),北京市自然科学基金(4202006)资助Supported by National Natural Science Foundation of China(62021003,61890930-5,61973010),National Key Research and Development Program of China(2021ZD0112302),and Natural Science Foundation of Beijing(4202006) （62021003,61890930-5,61973010）
