Use of the Neural Network Controller of Sprung Mass to Reduce Vibrations From Road Irregularities

Abstract

Hydraulic systems that damp active oscillation operate according to a certain non-linear and time-varying algorithm. It is difficult to create a controller based on its dynamic model. This chapter proposes a new operation regime of the controller based on neuron nets by combining the advantages of the adaptive, radial, and basic functions of the neuron net. Its undoubted advantages are a learning (tilting) ability in real time to process indefinite, nonlinear disturbances, and to change the value of the active force in the hydraulic leaf spring by adjusting the weight coefficients of the neuron net and/or the radial parameters of the basic function. The model is a ¼ hydraulic active sprung mass of a mobile vehicle. The modeling shows that the use of a neuron net controller makes the sprung mass much more efficient.