Event-Triggered Neural Network Multivariate Control for Wastewater Treatment Process

Recently, the neural network control has been widely used in the field of wastewater treatment process (WWTP). However, most neural network (NN) control methods are time-driven, with a large number of transmissions and a large amount of neural network computation. To reduce the number of controller executions and save computational cost, the event-triggered neural network multivariate method is proposed to control WWTP. Firstly, different from the traditional NN-based control, the event-triggered mechanism based on sliding windows is designed to reduce the computation. Then, the multi-input and multi-output recurrent wavelet neural network (RWNN) controller is proposed for simultaneous control of dissolved oxygen and nitrate nitrogen. Furthermore, the stability of the RWNN controller is analyzed through the Lyapunov stability theorem. Experimental results demonstrate that the event-triggered RWNN delivers a significant 25% reduction in the number of executions without compromising control accuracy.