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Author:

Cao, Yaxin (Cao, Yaxin.) | Qie, Qiuyue (Qie, Qiuyue.) | Wang, Gongming (Wang, Gongming.)

Indexed by:

EI Scopus

Abstract:

Because of highly complexity and large-scale operation, wastewater treatment process (WWTP) is considered as a robust adaptive control problem. This paper proposes a data-driven robust adaptive control with deep learning (DRAC-DL) for WWTP to improve the operational performance. In ths DRACDL framework, a robust controller is firstly designed to construct the closed-loop control scheme. Meanwhile, an adaptive deep belief network (ADBN) is developed based on the self-incremental learning strategy to approximate the ideal control law. The main advantage of DRAC-DL lies in its improved robustness and low computational burden, which benefit from Lyapunov-based closed-loop controller and efficient ADBN approximator. Finally, the effectiveness of DRAC-DL is demonstrated on the benchmark simulation model No.1 (BSMI) of WWTP. © 2022 IEEE.

Keyword:

Wastewater treatment Controllers Reclamation Process control Adaptive control systems Deep learning

Author Community:

  • [ 1 ] [Cao, Yaxin]Qufu Normal University, Library Management Centre, Qufu; 273165, China
  • [ 2 ] [Qie, Qiuyue]Army Aviation Institute of the PLA, Research Center of the UAV System, Beijing; 101123, China
  • [ 3 ] [Wang, Gongming]Beijing University of Technology, Beijing Institute of Artificial Intelligence, Beijing; 100124, China

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Source :

Year: 2022

Page: 1466-1470

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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