Neuro Model Approach for a Quarter Car Passive Suspension Systems

Abstract

The road handling, load carrying and passenger comfort are three intension factors on car suspension’s system. They should be compromised to achieve the good the car suspension dynamics. To fulfill the requirement, the car suspension system must be controlled and analyzed. To design and analyze the suspension controller, the realistic dynamics model of car suspension is needed. In this paper, the car suspension is assumed as a quarter car and has a model structure as a neural network structure. The model is assumed consist of nonlinear properties that are contributed by spring stiffness and damping elements of suspension system. The tire is assumed has linear properties and represented by spring stiffness element and damping element. The model responses are generated in simulation term. The random type of artificial road surface signal as an input variable is used in this simulation. The results show that the trend of neuro model have the same with the response of a quarter car nonlinear model from dynamic derivation. It means that the developed neuro model structure capable to represent the nonlinear model of a quarter car passive suspension system dynamics.