Particle Gibbs with Ancestor Sampling for Identification of Tire Friction Parameters
Abstract Particle Gibbs with Ancestor Sampling (PGAS) is a particle Markov chain Monte Carlo method (PMCMC) for Bayesian inference and learning. PGAS conditions on a reference state trajectory in the underlying particle filter using ancestor sampling. In this paper, we leverage PGAS for identification of cornering stiffness parameters in road vehicles only using production grade sensors. The cornering stiffness parameters are essential for describing the motion of the vehicle. We show how PGAS can be adapted to efficiently learn the stiffness parameters by conditioning on the noise input trajectory instead of the state trajectory. We verify on a three minute long experimental test drive that our method correctly identifies the tire stiffness parameters.