Key parameters in dynamic systems often change during their life cycle
due to repair and replacement of parts or environmental changes. This
paper presents a new approach to account for these changes by updatin
g the system models. Current iterative methods developed to solve the
model updating problem rely on minimisation techniques to find the set
of model parameters that yield the best match between experimental an
d analytical responses. These minimisation procedures require consider
able computation time, making the existing techniques infeasible for s
ome applications, such as in an adaptive control scheme, correcting th
e model parameters as the system changes. The proposed approach uses f
requency domain data and a neural network to estimate the updated para
meters quickly, yielding a model representative of the measured data.
Besides control-related applications, this may also be of use for manu
facturing systems, where parameters change during operation requiring
repeated updates of the nominal model. Numerical simulations and exper
imental results show that the neural network updating method (NNUM) ha
s good accuracy and generalisation properties, and it is therefore a s
uitable alternative for the solution of the model updating problem of
this class of systems. (C) 1998 Academic Press Limited.