Speed-gradient inverse optimal neural control for anaerobic digestion processes

In this paper, speed-gradient inverse optimal neural control for trajectory tracking is applied to an anaerobic digestion process. The control law calculates dilution rate and bicarbonate in order to track a methane reference trajectory determined to increase methane production under controlled conditions and avoid washout. A nonlinear discrete-time neural observer for unknown nonlinear systems in presence of external disturbances and parameter uncertainties is used to estimate the biomass concentration, substrate degradation and inorganic carbon. This observer is based on a discrete-time recurrent high-order neural network trained with an extended Kalman filter (EKF) based algorithm; it allows the applicability of inverse optimal neural control. The applicability of the proposed scheme is illustrated via simulations.