Reinforcement learning control with n-step information for wastewater treatment systems.

Wastewater treatment is important for maintaining a balanced urban ecosystem. To ensure the success of wastewater treatment, the tracking error between the crucial variable concentrations and the set point needs to be minimized as much as possible. Since the multiple biochemical reactions are involved, the wastewater treatment system is a nonlinear system with unknown dynamics. For this class of systems, this paper develops an online action dependent heuristic dynamic programming (ADHDP) algorithm combining the temporal difference with λ [TD(λ)], which is called ADHDP(λ). By introducing the TD(λ), the future n -step information is considered and the learning efficiency of the ADHDP algorithm is improved. We not only give the implementation process of the ADHDP(λ) algorithm based on neural networks, but also prove the stability of the algorithm under certain conditions. Finally, the effectiveness of the ADHDP(λ) algorithm is verified through two nonlinear systems, including a wastewater treatment system and a torsional pendulum system. Simulation results show that the ADHDP(λ) algorithm has higher learning efficiency compared to the general ADHDP algorithm. [ABSTRACT FROM AUTHOR]