Neuro-leaf spring

A recurrent neural network is taught to emulate a leaf spring that is typic ally employed in the suspension system of trucks. Leaf springs are known to have nonlinear and hysteresis behaviour. This makes their mathematical for mulation difficult and susceptible to a considerable amount of estimation e rrors. Analysis of the vehicle's dynamic behaviour is heavily reliant on th e accurate determination of the suspension forces. It is shown that the rec urrent neural network is able to emulate the leaf spring behaviour very acc urately after it is taught with a set of input output data points. In order to generate the teaching data points an analytical model of the leaf sprin g is used. The performance of the developed neural network emulator is also evaluated in the time and frequency domains and compared to those of the a nalytical model.