A Novel Robust Control for Disturbed Uncertain Microbial Fuel Cell with Noisy Output

M icrobial fuel cell (M FC) is one of the most important renewable sources for energy supply and reduction of environmental pollution, which has affected by various adversities due to operating conditions.In this paper, there are several serious issues related to the stable operation of a microbial fuel cell that have considered in the design of the controller, including: 1-Nonlinear terms that are of hard type; 2-Uncertainty of the model which is of parametric type and includes changes in temperature, environment and concentration; 3-Disturbances into the system which are of both matched and unmatched types; 4-And noise on the fuel cell output which has different origins.Also, the nonlinear model of M FC has considered for a more accurate description of system dynamics.By using of output feedback, adaptive, and sliding mode methods, and developing an approximation based on chebyshev neural network, a novel robust hybrid technique has proposed for controlling M FC output voltage and power.Using chebyshev neural network which has a simple structure with a suitable computational volume, the uncertainties, disturbances and hard nonlinear terms have approximated, and the optimal weights of the approximation have obtained by designing adaptive laws.Also, the robust part of the controller eliminates the effects of estimation error and noise.The Lyapunov' s theory has used to ensure the stability of the closed-loop system.Furthermore, simulation in M ATLAB environment and making comparison with the recent three robust methods in a strong scenario shows the efficiency of the proposed control method.