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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.
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年份: 2022
页码: 1466-1470
语种: 英文
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