On Vehicle Suspension Dynamics

1. Introduction

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.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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