Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

被引：0

作者：

Kandel, Aaron ^{[1
]}

Wahba, Mohamed ^{[2
]}

Fathy, Hosam K. ^{[3
]}

机构：

[1] Department of Mechanical Engineering, University of California, Berkeley, Berkeley, 94704, CA

[2] Department of Mechanical Engineering, The Pennsylvania State University, University Park, 16801, PA

[3] Department of Mechanical Engineering, University of Maryland, College Park, 20742, MD

来源：

ASME Letters in Dynamic Systems and Control | 2022年 / 2卷 / 02期

关键词：

estimation; identification; vehicle dynamics and control;

D O I：

10.1115/1.4052990

中图分类号：

学科分类号：

摘要：

This article investigates the theoretical Cramér-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this article presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future. Copyright © 2021 by ASME.

引用

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