Date of Award
2022
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Mechanical, Automotive, and Materials Engineering
Keywords
Adaptive suspension system, EoM, Neural network, ; Simulink
Supervisor
B. Minaker
Supervisor
J. Johrendt
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Suspension systems in the auto industry have always been a topic of interest, as they relate to so many aspects of vehicles. Various types of suspension are commonly used now, such as passive suspensions, semi-active suspensions and active suspensions. However, the current technology mainly focuses on the change of damping ratio. The aim of this thesis is to consider both spring and damper properties for suspensions of an off-road vehicle. In order to do this, a 10-degree of freedom model was built using the EoM software in Julia. The output state space matrices from EoM were used as an input in a Matlab/Simulink control loop to analyze the performance. A critical goal was to see the effects on ride comfort of using Neural Networks for property selection in the Matlab/Simulink control loop. By adding extra spring forces and damping forces, the level of ride comfort was modified. Preliminary road tests were conducted to serve as a proof of concept.
Recommended Citation
Zhixian, Sylvia Yin, "Effects on Vehicle Ride Comfort of an Adaptive Suspension System Using Neural Networks" (2022). Electronic Theses and Dissertations. 9015.
https://scholar.uwindsor.ca/etd/9015