基于强化学习的自适应事件触发控制在污水处理过程中的应用

To enhance the control effectiveness and operational efficiency of the wastewater treatment process (WWTP), this article proposes a multivariable optimal control scheme based on an identifier–critic–actor reinforcement learning (RL) framework with an event-triggered mechanism (ETM) for dissolved oxygen (DO) and nitrate nitrogen (NO) concentrations. First, the first fuzzy neural network (FNN) is used to estimate the unknown dynamics in WWTP, and the second FNN is implemented within the critic–actor optimization framework. Second, the RL algorithm is applied to design optimal controllers for DO and NO concentrations by constructing a tangent barrier Lyapunov function. Moreover, a dynamic ETM based on the adaptive threshold strategy is proposed to balance the control performance and energy consumption in the wastewater system. Finally, stability analysis and the benchmark simulation model no. 1 are conducted to verify that the control scheme proposed in this article demonstrates effectiveness and enhanced performance.