Vehicle parameter estimation using a model-based estimator

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• 作者：Giulio Reina^a ; ^{giulio.reina@unisalento.it} ; Matilde Paiano^a ; Jose-Luis Blanco-Claraco^b
• 关键词：Vehicle state estimation ; Extended Kalman filter ; Mass estimation ; Adaptive estimation ; Vehicle lateral dynamics
• 刊名：Mechanical Systems and Signal Processing
• 出版年：2017
• 出版时间：15 March 2017
• 年：2017
• 卷：87
• 期：part_PB
• 页码：227-241
• 全文大小：1619 K
• 卷排序：87

文摘

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.