Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process

Abstract

Due to high complexity and time-variant operation, as well as increasingly requirements for water quality, stability and reliability, wastewater treatment process (WWTP) is regarded as an adaptive control problem. In this study, a data-driven adaptive control with deep learning (DRAC-DL) is developed to improve the operational performance of WWTP. First, a feedback controller is designed to construct the closed-loop control scheme. Second, an adaptive deep belief network (ADBN), based on the data-driven self-incremental learning strategy, is proposed to approximate the ideal control law. Third, the stability of DRAC-DL scheme is analyzed in detail. The main advantage of DRAC-DL lies in its improved robustness and efficiency, which benefit from Lyapunov-based closed-loop strategy and efficient ADBN controller. Finally, the feasibility and applicability of DRAC-DL are verified by two parts: 1) Simulation on nonlinear system; and 2) Application to WWTP on the benchmark simulation model No.1 (BSM1). The experimental results show the applicability and effectiveness, among which DRAC-DL reduces the output fluctuation (Var) by no less than 82% and realizes the better stability and robustness.