文章基本信息

标题：Particle Gibbs with Ancestor Sampling for Identification of Tire-Friction Parameters
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作者：Karl Berntorp ; Stefano Di Cairano
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：14849-14854
DOI：10.1016/j.ifacol.2017.08.2585
语种：English
出版社：Elsevier
摘要：AbstractParticle 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.
关键词：KeywordsParticle filterMonte Carlo methodFriction estimationSystem identification