Data-Based Robust Adaptive Dynamic Programming for Balancing Control Performance and Energy Consumption in Wastewater Treatment Process

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.