Robust Type-2 Fuzzy Neural Control for Wastewater Treatment Process With External Disturbances

The severe influence of external disturbances makes it difficult to keep the tracking error of the wastewater treatment process (WWTP) within a given range. Therefore, it is a challenging task to design the controller to realize robust bounded tracking control for WWTP. To solve this problem, a robust type-2 fuzzy neural control (RT2FNC) strategy is designed in this paper. The main contributions of the proposed RT2FNC strategy are threefold. First, an estimation model of interval type 2 fuzzy neural network (IT2FNN) with adaptive update strategy in RT2FNC is developed to identify the unknown dynamics of WWTP. Then, the high robustness of the IT2FNN estimation model is utilized to achieve accurate estimation of WWTP within external disturbances. Second, a type 2 fuzzy neural control algorithm based on nonlinear mapping (NM) method is proposed to consider the transformation of the aeration and denitrification processes into an unconstrained problem. Then, the tracking errors of dissolved oxygen and nitrate nitrogen can be guaranteed to be within the specified range. Third, the stability of the RT2FNC strategy is analyzed and demonstrated. Then, the successful application of the developed method is guaranteed. Finally, the simulation results tested on benchmark simulation model 1 (BSM1) verify the effectiveness of the proposed RT2FNC strategy with good robust bounded tracking performance within external disturbances.