Adaptive Robust Fuzzy Sliding Mode Control for Wastewater Treatment Processes

The control of the wastewater treatment processes (WWTPs) is considered as an adaptive robust control problem due to the presence of high nonlinearity and disturbances. In this article, an adaptive robust fuzzy sliding mode control (ARFSMC) strategy for WWTPs is proposed to guarantee the operational performance. First, considering the high nonlinearity of the WWTPs, a fuzzy neural network (FNN) is employed to identify the system dynamics. And the identification results are directly used for controller design. To address the effect of disturbances on identification accuracy, a sliding mode observer has been designed, where the observation error is utilized to adjust the adaptive law of the FNN identifier. Second, a variable parameters disturbance observer is devised to accurately estimate time-varying disturbances in the WWTPs. The observer is used to build a dynamic model by estimating the state, thereby obtaining disturbance as an additional state. And the parameters within the observer are dynamically adjusted using a state estimation error-based parameter adjustment law. Third, an adaptive switching gain sliding mode controller, combined with the disturbance observer, is designed to improve the stability of the system. Specifically, the switching gain is determined by subtracting the disturbance observer output from the estimated disturbance upper bound. Finally, the experimental results on the BSM1 demonstrate that ARFSMC has superior control performance compared to existing methods.