Event-Based Approximate Neuro-Optimal Tracking Control Design Involving a Wastewater Treatment Application

Water resources shortage can be availably relieved by the wastewater treatment. In this paper, we develop a novel event-based near-optimal tracking control algorithm for a class of unknown dynamic nonaffine systems, with the purpose of improving the control performance of the dissolved oxygen concentration and the nitrate nitrogen concentration in the nonlinear wastewater treatment plant. First, the classical heuristic dynamic programming (HDP) method is established to solve the near-optimal tracking control problem. Then, the event-triggered mechanism is introduced to reduce the communication burden in the traditional tracking control design. Meanwhile, the method can ensure that the closed-loop system has an acceptable performance in application. In addition, we provide three kinds of neural networks for the implementation of the event-based HDP algorithm. Finally, a wastewater treatment application is chosen to testify the superior performance of the proposed approach.