4.8 Article

Fault Effect Identification-Based Adaptive Performance Self-Recovery Control Strategy for Wastewater Treatment Process

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出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2023.3296878

关键词

Actuators; Uncertainty; Fuzzy neural networks; Fuzzy logic; Process control; Artificial neural networks; Sliding mode control; Adaptive control; constraint imitation; fault effect identification; performance self-recovery control; wastewater treatment process (WWTP)

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The increasing utilization of wastewater requires dedicated attention to potential security threats and the formulation of strategies for defense, response, and future protection. This article proposes an adaptive performance self-recovery control strategy for wastewater treatment processes with nonideal actuators. The strategy enhances the faulty performance self-recovery capability of the system while ensuring robust output regulation and fast convergence.
The increasing utilization of wastewater necessitates dedicated attentions to the potential security threats, and formulate strategies for defense, response, and future protection. The nonideal actuator subject to the faults and constraints may underload the driving force and reduce the sewage purification efficiency. This article proposes an adaptive performance self-recovery control strategy for the wastewater treatment process (WWTP) with nonideal actuator. Therein, a Gaussian error function is reconstructed to imitate the asymmetrical actuator constraints. A fault effect identifier is designed to indirectly acquire fault information. Two boundary estimators are co-designed to estimate the infimum of virtual controller gain and the supremum of lumped uncertainty, respectively. The proposed control strategy can largely enhance the faulty performance self-recovery capability of the WWTP, while ensuring robust output regulation and fast convergence. Extensive experiments on dissolved oxygen control are executed on a WWTP platform to show the efficacy of the suggested control scheme.

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