VEHICLE SUSPENSION SYSTEM USING SYSTEM IDENTIFICATION

This report basically discusses the basic understanding of the chosen topic, which is Vehicle Suspension System Using System Identification. It is also discusses the findings on equation of the simplified single spring suspension. Initiation of this project started when physical modeling of the susp...

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Bibliographic Details
Main Author: BAHADON, ANUM SURAYA BINTI
Format: Final Year Project
Language:English
Published: Universiti Teknologi Petronas 2009
Subjects:
Online Access:http://utpedia.utp.edu.my/4088/1/SoftBound-aNum.pdf
http://utpedia.utp.edu.my/4088/
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Summary:This report basically discusses the basic understanding of the chosen topic, which is Vehicle Suspension System Using System Identification. It is also discusses the findings on equation of the simplified single spring suspension. Initiation of this project started when physical modeling of the suspension system is costly and too time consuming. Therefore, a half front-rear suspension system simulation is needed to evaluate the optimum parameters to be used. The objective of the project is to find the best possible system identification model structures for the vehicle suspension system with the usage of Matlab System Identification Toolbox. The challenge is to get the possibilities of predicting the suspension model for the simulation of half front-rear suspension. The road surface will be introduced as inputs of the system that will be modeled. The simple model of suspension is used as a reference for further research on the half front-rear suspension system. The basic suspension’s equation for quarter-car model is determined to develop a Matlab simulation block diagram. From the quarter-car model, the system is developed to half front-rear model for further analysis. With the half front-rear model simulation, all the parameters which also include the Proportional Integral Derivative (PID) controller gain of the system can be tested. Simulation study shows second order of Auto Regression with Extra Input (ARX) model gives the most promising result in identifying the system.