Abstract
A recurrent neural network is taught to emulate a leaf spring that is typically employed in the suspension system of trucks. Leaf springs are known to have nonlinear and hysteresis behaviour. This makes their mathematical formulation difficult and susceptible to a considerable amount of estimation errors. Analysis of the vehicle's dynamic behaviour is heavily reliant on the accurate determination of the suspension forces. It is shown that the recurrent neural network is able to emulate the leaf spring behaviour very accurately 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 spring 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 analytical model.
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