NONLINEAR IDENTIFICATION OF THE PCO2 CONTROL-SYSTEM IN MAN

Two approaches to identification of the PCO2 system in man are describ ed. The first uses a nonlinear 'black box' NARMAX identification packa ge, while the second method uses a structured two-compartment Belville model. The data were obtained from volunteers breathing either room a ir or a controlled gas mixture, controlled via a pseudorandom M-sequen ce. Measurements were made of respiratory gas flow and PCO2 content of inspired and expired gases. The identification results indicate that a low-order dynamic model with nonlinear polynomial expansion gave the best fit to the data. In contrast, the Belville model gave best resul ts with a two-compartment linear model, mainly because of difficulties in the optimisation routines when the Belville model was not linear. Thus, modem systemic methods of excitation and identification appear t o be appropriate for modelling this respiratory subsystem of humans.