Vehicle parameter estimation using a model-based estimator

被引：74

作者：

Reina, Giulio ^{[1
]}

Paiano, Matilde ^{[1
]}

Blanco-Claraco, Jose-Luis ^{[2
]}

机构：

[1] Univ Salento, Dept Engn Innovat, Via Arnesano, I-73100 Lecce, Italy

[2] Univ Almeria, Dept Engn, Almeria 04120, Spain

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2017年 / 87卷

关键词：

Vehicle state estimation; Extended Kalman filter; Mass estimation; Adaptive estimation; Vehicle lateral dynamics; MASS;

D O I：

10.1016/j.ymssp.2016.06.038

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In the last few years, many closed-loop control systems have been introduced in the automotive field to increase the level of safety and driving automation. For the integration of such systems, it is critical to estimate motion states and parameters of the vehicle that are not exactly known or that change over time. This paper presents a model-based observer to assess online key motion and mass properties. It uses common onboard sensors, i.e. a gyroscope and an accelerometer, and it aims to work during normal vehicle manoeuvres, such as turning motion and passing. First, basic lateral dynamics of the vehicle is discussed. Then, a parameter estimation framework is presented based on an Extended Kalman filter. Results are included to demonstrate the effectiveness of the estimation approach and its potential benefit towards the implementation of adaptive driving assistance systems or to automatically adjust the parameters of onboard controllers. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：227 / 241

页数：15

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