9 articles in this category
Suspension data is noisy. Not just sensor noise—process noise. The stuff that comes from how you mount a string pot, how fast you log, or whether you ...
You've built a beautiful multibody suspension model. The hardpoints are dialed. The bushings are characterized to 0.1 mm. But when you run a complianc...
So you've got two data-driven bump steer models—maybe one from a neural net, another from a polynomial fit. Both claim low error. But which one actual...
It's 4 p.m. on a test day. You're looking at two curves that should overlap — but they don't. The kinematic hardpoint model says the camber gain shoul...
So you're designing a real-time damper controller—maybe for an active suspension on a race car, maybe for a semi-active setup on a luxury SUV. The phy...
You spend weeks building a suspension kinematics model. You check every bushing compliance, every pickup point. The simulation hands you a perfect lin...
You run a group of physical tests on the new suspension rig. The results come back — and they don't match your data-driven model. Worse, the model has...
You have two models. One is a neural net trained on 10 million points from a Kistler road simulator. The other is a sparse Gaussian process fitted to ...
Suspension tuning is a trade-off between accuracy and speed. If you have ever spent hours watching a solver iterate without converging, you know the p...