Neural Network Control for a Semi-Active Vehicle Suspension with a Magnetorheological Damper

Abstract

Semi-active vehicle suspension with magnetorheological dampers is a promising technology for improving the ride comfort of a ground vehicle. However, the magnetorheological damper always exhibits nonlinear hysteresis between its output force and relative velocity, and additional nonlinear stiffness owing to the state transition from liquid to semi-solid or solid, so that the semi-active suspension with magnetorheological dampers features nonlinearity by nature. To control such nonlinear dynamic systems subject to random road roughness, in this paper we present a neural network control, which includes an error back propagation algorithm with quadratic momentum of the multilayer forward neural networks. Both the low frequency of road-induced vibration of the vehicle body and the fast response of the magnetorheological damper enable the neural network control to work effectively on-line. The numerical simulations and an experiment for a quarter-car model indicate that the semi-active suspension with a magnetorheological damper and neural network control is superior to the passive suspensions in a range of low frequency.