PSO hybrid intelligent inverse optimal control for an anaerobic process

Abstract

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 law calculates dilution rate and bicarbonate supply, and a Takagi-Sugeno supervisor based on the estimation of biomass, selects and applies the most adequate control action, allowing a smooth switching between open loop and closed loop. A Particle Swarm Optimization (PSO) algorithm is employed to determine the matrix P for inverse optimal control in order to improve tracking results. The applicability of the proposed scheme is illustrated via simulations.