Fuzzy super-twisting sliding mode control for municipal wastewater nitrification process

A fuzzy super-twisting algorithm sliding mode controller is developed for the dissolved oxygen concentration in municipal wastewater nitrification process. First, a fuzzy neural network (FNN) model is designed to approach the oxygen dynamics with unmeasurable disturbances, then the established model consists of the nominal system model and the modelling error. Second, based on the FNN model, a super-twisting sliding mode controller is employed to stabilize the nominal system and to suppress the modelling error. Moreover, the stability of the system is investigated and an adaption law is applied to ensure the robustness of the closed-loop system. Finally, the comparison experiments on benchmark simulation model no. 2 (BSM2) of wastewater treatment show the advantages of the proposed method in multiple-units oxygen concentration control.

Automated summary

A fuzzy super-twisting algorithm sliding mode controller is developed for controlling the dissolved oxygen concentration in municipal wastewater nitrification process. The controller utilizes a fuzzy neural network model to approximate the oxygen dynamics and employs a super-twisting sliding mode controller to stabilize the system and suppress the modeling error. Experimental results on wastewater treatment benchmark simulation model no. 2 (BSM2) demonstrate the advantages of the proposed method in multiple-units oxygen concentration control.