Adaptive Interval Type-2 Fuzzy-Neural Sliding Mode Control for WWTP with External Disturbance

In this paper, an adaptive interval type-2 fuzzy-neural sliding mode control (AT2FSC) method using a disturbance observer is developed for wastewater treatment process (WWTP) with unknown external disturbance. Firstly, to overcome the modeling complexity of WWTP, a simplified interval type-2 fuzzy neural network (IT2FNN) is used to approximate the uncertainty dynamics of WWTP. Then, the network update parameters are reduced while ensuring the modeling accuracy. Secondly, the nonlinear disturbance observer is designed in AT2FSC to estimate the unknown external disturbance. Then, the robustness of AT2FSC under the influence of external disturbances can be guaranteed. Thirdly, the adaptive sliding mode gain is designed to achieve stable control of WWTP. The Lyapunov theory has finally demonstrated that the proposed AT2FSC approach can guarantee system stability. The outcomes of the simulation demonstrate the ability of the proposed AT2FSC approach to provide efficient control performance and suppress external disturbances in WWTP.

I. Introduction

The lack of freshwater resources is an important issue that restricts the development of many countries [1]-[2], and freshwater resources can be largely replenished through wastewater purification and reuse. Therefore, wastewater treatment process (WWTP) as an effective purification method to remove pollutants through biochemical reactions has received much attention [3]-[4]. The control performance in WWTP has a significant impact on the purification quality. An important topic for research is the efficient regulation of WWTP [5]-[8].