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Neuroadaptive Fuzzy Dynamic Optimal Tracking Control in Wastewater Treatment Aeration Process
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DOI：10.1109/tfuzz.2026.3698051.png)
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Abstract
En 中文
The optimal control method is a valuable technique for improving the safety and reliability of the aeration process in the wastewater treatment process (WWTP). However, due to the complex and time-varying characteristics in WWTP, it is difficult to design the adaptive optimal controller to guarantee the system stability and reduce operating energy consumption. Therefore, a neural network approximation-based adaptive fuzzy dynamic optimal tracking control method is designed to real-time control the dissolved oxygen concentration (DOCN). First, due to the time-varying nonlinearity of WWTP, an inverse fuzzy dynamic multiobjective evolutionary algorithm based on decomposition (MOEA/D) algorithm is designed to acquire the optimal setpoints of DOCN. Second, an actor network is utilized to identify uncertain dynamic situations, and a critic network is utilized to minimize the utility function, which can improve the identification accuracy. Meanwhile, an adaptive auxiliary signal, specifically calibrated to dead-zone parameters, is constructed to counteract the influence of an asymmetric dead-zone on control performance. Finally, the adaptive optimal controller is used to achieve precise tracking control of DOCN in a WWTP. The stability of the control system is proved, and the effectiveness is evaluated in benchmark simulation model I.
Keywords:
Actuator dead-zone
adaptive optimal control
fuzzy neural network (FNN)
inverse modeling
wastewater treatment aeration process
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