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.
Note to Practitioners
—The motivation of this paper is to overcome the influence of external disturbances of WWTP on the control accuracy. We propose a robust type-2 fuzzy neural control (RT2FNC) strategy to ensure that the tracking errors of dissolved oxygen (DO) and nitrate nitrogen (NO
$_3$
-N) of WWTP under external disturbances can be maintained within a certain range. In this paper, a critical analysis and discussion on the improvement of the control accuracy is provided and we present a successful implementation of the RT2FNC strategy on performance evaluation, which can be regarded as a significant attempt for engineering practice. Considering the complexity and uncertainty of the controlled objects in WWTP, the implementation of the RT2FNC method consists of three aspects: First, an adaptive type-2 fuzzy neural network identifier is established to achieve accurate estimation of WWTP dynamics. Then, a type-2 fuzzy neural control algorithm based on nonlinear mapping is proposed to ensure the boundedness of tracking control by using the transformation error. Third, a robust compensator is designed to overcome external disturbances and ensure the stability. Finally, the experiments on the WWTP benchmark platform verify that the RT2FNC strategy has high control accuracy and robustness under external disturbances, meanwhile helping practitioners improve the reliability of WWTP operation.