The suspension system is one of the most sensitive systems of a vehicle as it affects the dynamic behavior of the vehicle with even minor changes. These systems are designed to carry out multiple tasks such as isolating the vehicle body from the road/tire vibrations as well as achieving desired ride and handling performance levels in both steady state and limit handling conditions. The damping coefficient of the damper plays a crucial role in determining the overall frequency response of the suspension system. Considerable research has been carried out on semi active damper systems as the damping coefficient can be varied without the system requiring significant external power giving them advantages over both passive and fully active suspension systems.
Dampers behave as non-linear systems at higher frequencies and hence it has been difficult to develop accurate models for its full range of motion. This study aims to develop a velocity sensitive damper model using artificial neural networks and essentially provide a 'black-box' model which encapsulates the non-linear behavior of the damper. A feed-forward neural network was developed by testing a semi active damper on a shock dynamometer at CenTiRe for multiple frequencies and damping ratios. This data was used for supervised training of the network using MATLAB Neural Network Toolbox. The developed NN model was evaluated for its prediction accuracy. Further, the developed damper model was analyzed for feasibility of use for simulations and controls by integrating it in a Simulink based quarter car model and applying the well-known skyhook control strategy. Finally, effects on ride and handling dynamics were evaluated in Carsim by replacing the default damper model with the proposed model. It was established that this damper modeling technique can be used to help evaluate the behavior of the damper on both component as well as vehicle level without needing to develop a complex physics based model. This can be especially beneficial in the earlier stages of vehicle development. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/78295 |
| Date | 30 June 2017 |
| Creators | Bhanot, Nishant |
| Contributors | Mechanical Engineering, Taheri, Saied, Sandu, Corina, Ferris, John B. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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