On Vehicle Suspension Dynamics
Ramesh Chen
Corresponding
Tokyo Institute of Technology
Rafiqul García
University of Toronto
Abstract
As industrial automation continues to expand, ensuring the reliability of vehicle suspension dynamics grows steadily more important. In this study, Kalman filtering was employed to analyse and characterise the system under investigation. The model predicted vehicle suspension dynamics with an average error of less than 40%. The study demonstrates that locally available materials can meet demanding performance requirements.
Keywords:
vehicle suspension dynamicsKalman filteringharmonic analysiscognitive radiocryptographycomputational fluid dynamics
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