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.