能源消耗
人工神经网络
水准点(测量)
控制理论(社会学)
计算机科学
李雅普诺夫函数
鲁棒控制
过程(计算)
自适应控制
控制工程
控制系统
数学优化
工程类
控制(管理)
人工智能
非线性系统
数学
物理
大地测量学
量子力学
地理
电气工程
操作系统
作者
Weiwei Cao,Qinmin Yang,Wenchao Meng,Shuzong Xie
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-9
被引量:1
标识
DOI:10.1109/tii.2023.3346468
摘要
To promote the efficiency and economy of wastewater treatment process (WWTP), a novel data-driven robust adaptive dynamic programming (RADP) algorithm is proposed to balance the control performance and energy consumption. Action neural network and critic neural network constitute the proposed method, both the control signal and system error are simultaneously considered as part of cost function for lower energy consumption and better guaranteed performance. Furthermore, a robust item is designed to suppress the unknown disturbances of WWTP system and environment. The introduced method requires no prior knowledge of WWTP, and continuously updates the control law with the input–output data from WWTP system via the least squares algorithm. Moreover, the Lyapunov theorem validates the stability of controlled system. The systematic simulations based on benchmark simulation model No. 1 are performed to verify the superiority of the proposed RADP method compared with other methods that can achieve a significant reduction in energy consumption of aeration and pumping while maintaining the control performance.
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