A Recurrent Neural Network Modeling for Automotive Magnetorheological Fluid Shock Absorber

Automotive Magnetorheological (MR) fluid shock absorbers have been previously characterized by a series of nonlinear differential equations, which have some difficulties in developing control systems. This paper presents a recurrent neural network with 3 input neurons, 1 output neuron and 5 recurrent neurons in the hidden layer to simulate behavior of MR fluid shock absorbers to develop control algorithms for suspension systems. A recursive prediction error algorithm has been applied to train the recurrent neural network using test data from lab where the MR fluid shock absorbers were tested by the MTS electro-hydraulic servo vibrator system. Training of neural network model has been done by means of the recursive prediction error algorithm presented in this paper and data generated from test in laboratory. In comparison with experimental results of MR fluid shock absorbers, the neural network models are reasonably accurate to depict performances of MR fluid shock absorber over a wide range of operating conditions.