Parameter Estimation of Shock Absorbers with Artificial Neural Networks

A method for identification of adjustable shock absorbers is presented which combines a modern QRRLS parameter estimation algorithm (DSFI) with an artificial neural network (ANN) for classification purposes. The parameter estimation algorithm is based on a discrete-time linear model. Thus, no state variable filter (SVF) as for continuous time identification problems is required. For the ANN, a multilayer feedforward perceptron trained by backpropagation is used. The method was tested by simulation and with data drawn from shock absorber test stands at UC Berkeley and TU Darmstadt.