Date of Award


Publication Type


Degree Name



Mechanical, Automotive, and Materials Engineering


B. Minaker


Adaptive suspension system, EoM, Neural network, ; Simulink



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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.