Fuzzy Neural Network Based Tracking Control of Dissolved Oxgen in WWTP

Wastewater treatment process (WWTP) is a complex industrial process with strong nonlinear and time-varying dynamic characteristics. Dissolved oxygen (DO) concentration is a main factor limiting the effluent quality. Due to the complex biochemical reactions, designing an effective controller for this kind of process is a huge challenge. To achieve efficacious control under actuator saturation, a self-organizing fuzzy neural network adaptive tracking control method is proposed. Firstly, a structured model of actuator saturation is employed to ensure the prescribed steady-state and transient tracking performance. Secondly, the self-organizing fuzzy neural network is used to identify the unknown dynamics in WWTP. Then, the structure learning algorithm with correlation entropy is used to adjust the structure online. Thirdly, the stability of the control strategy is analyzed and the corresponding stability conditions are given. Finally, the simulation results on benchmark simulation model 1 (BSM 1) verify the effectiveness of the control method.