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In this paper, a neuro-fuzzy control strategy composed by a neural observer and fuzzy supervisors for an anaerobic digestion process is proposed in order to maximize methane production. A nonlinear discrete-time recurrent high order neural observer (RHONO) is used to estimate biomass concentration and substrate degradation in a continuous stirred tank reactor. A Takagi-Sugeno supervisor controller...
This paper proposes a hybrid intelligent inverse optimal control for trajectory tracking based on a neural observer and a fuzzy supervisor for an anaerobic digestion process, in order to maximize methane production. A nonlinear discrete-time recurrent high order neural observer (RHONO) is used to estimate biomass concentration and substrate degradation in a continuous stirred tank reactor. The control...
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...
In this paper, a dynamic learning rate, for recurrent high order neural observer (RHONO), is proposed. The dynamic learning rate depends on the pH on-line measurement. The main objective is to improve learning of the neuronal network in presence of disturbances, which is obtained by increasing the performance of the neuronal observer by means of the dynamic learning rate. The learning algorithm is...
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