Robust Adaptive Sliding Mode Control for Microbial Fuel Cells Based on Neural Networks

Microbial fuel cells (MFCs) have emerged as a significant biotechnological tool for clean energy generation in recent years. However, due to the intricate interplay between microbial populations, electrochemical reactions, and electron transfer processes, MFC systems exhibit pronounced nonlinear characteristics. Specifically, fluctuations in external load conditions lead to variations in system voltage and power density, which significantly impact the overall power generation performance. To address the challenges of unstable output voltage and low power density caused by external load disturbances during power generation, this paper presents an adaptive sliding mode control (SMC) method based on neural networks (NN). First, this approach suppresses the adverse effects of load disturbances through an adaptive mechanism. Subsequently, the neural network algorithm is employed to approximate uncertain terms within the sliding mode control framework and Lyapunov stability analysis ensures a convergent control scheme. Finally, simulation results demonstrate that the proposed method outperforms traditional sliding mode control and backstepping control methods in effectively managing external load disturbances and optimizing the power generation performance of MFCs.