Fuzzy Neural Network-Based Multivariable Constrained Control for Wastewater Treatment Process With Actuator Failures

The wastewater treatment process (WWTP) operates under diverse conditions, and precise control of dissolved oxygen (DO) and nitrate nitrogen (NO3-N) concentrations is crucial for satisfying water quality standards. This paper proposes an adaptive fuzzy neural network (FNN) constrained control method based on an event-triggered mechanism. Firstly, the unknown nonlinear dynamic functions encountered in WWTP are effectively approximated using the FNN’s strong adaptive capability. Secondly, an event-triggered control (ETC) strategy is introduced to reduce the communication burden, with trigger conditions designed based on the control signal error. Subsequently, a time-varying asymmetric barrier Lyapunov function (BLF) is used to construct controllers for DO and NO3-N concentrations, ensuring variables remain within time-varying constraint ranges. Meanwhile, a fault-tolerant control (FTC) approach is introduced to cope with potential actuator failures during the WWTP. Finally, simulations using Benchmark Simulation Model 1 (BSM1) are performed to verify the effectiveness of the proposed method.