Fuzzy Neural Network-Based Robust Model-Free Adaptive Fault-Tolerant Control for Wastewater Treatment Process

In wastewater treatment process (WWTP), the dissolved oxygen concentration (DOC) sensor fault provides incorrect data to the control system, affecting the blower operation. This leads to insufficient aeration and increases the risk of membrane fouling. To solve this problem, a robust model-free fault-tolerant controller (RMFFTC) is designed. First, the pseudo partial derivative (PPD) approach is utilized to transform nonlinear WWTP into a compact form dynamic linearization (CFDL) data model with residual disturbances. Then, according to the CFDL model, a robust fault detection threshold is designed by the extended state observer (ESO) to timely detect the occurrence of faults. Second, after detecting the DOC sensor fault, the fault is estimated using the fuzzy neural network (FNN) considering that the fault is unknown. Third, an improved RMFFTC is designed based on the fault estimation information. In particular, to ensure stable DOC tracking under sensor faults, the controller design considers the output tracking error variation. In addition, the bounded-input–bounded-output (BIBO) stability result is provided to theoretically guarantee the usefulness of the proposed RMFFTC for DOC control affected by the sensor fault. Finally, the effectiveness of the RMFFTC are verified through extensive simulations in the membrane bioreactor (MBR) model.