On model updating using neural networks

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 updating 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 and analytical responses. These minimisation procedures require considerable computation time, making the existing techniques infeasible for some applications, such as in an adaptive control scheme, correcting the model parameters as the system changes. The proposed approach uses frequency domain data and a neural network to estimate the updated parameters quickly, yielding a model representative of the measured data. Besides control-related applications, this may also be of use for manufacturing systems, where parameters change during operation requiring repeated updates of the nominal model. Numerical simulations and experimental results show that the neural network updating method (NNUM) has good accuracy and generalisation properties, and it is therefore a suitable alternative for the solution of the model updating problem of this class of systems. (C) 1998 Academic Press Limited.