Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics Article Swipe

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Mark Wielitzka , Alexander Busch , Matthias Dagen , Tobias Ortmaier ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.5772/intechopen.71900

Advanced driver assistance systems in modern vehicles have gained interest in the past decades. For most of these systems accurate knowledge about the current driving state, describing the vehicle's stability, and certain parameters is beneficial for improved performance. Especially, a robust estimation of the vehicle's side-slip angle, and, furthermore, knowledge about some influential system parameters, like the vehicle's mass or its moment of inertia, has vast potential to improve the state estimation's accuracy and, therefore, improve the assistance system's performance. In this paper an online estimation of the vehicle's side-slip angle and additional estimation of the mass and moment of inertia, separately and simultaneously is presented using the joint Unscented Kalman Filter. The state estimation results are validated by comparing to measurements taken on a VW Golf VII. The parameter estimation results are verified by comparing to results obtained using a global offline identification algorithm.

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Estimation

Computer Science

Engineering

Artificial Intelligence

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Kalman filter Control theory (sociology) Extended Kalman filter Moving horizon estimation Estimation Fast Kalman filter Computer science Unscented transform Estimation theory Engineering Artificial intelligence Algorithm Control (management) Systems engineering

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Type: book-chapter
Language: en
Landing Page: https://doi.org/10.5772/intechopen.71900
PDF: https://www.intechopen.com/citation-pdf-url/57977
OA Status: hybrid
Cited By: 18
References: 17
Related Works: 10
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DOI: https://doi.org/10.5772/intechopen.71900

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Title: Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

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Type: book-chapter

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Language: en

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Publication year: 2018

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Publication date: 2018-02-21

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Authors: Mark Wielitzka, Alexander Busch, Matthias Dagen, Tobias Ortmaier

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Landing page: https://doi.org/10.5772/intechopen.71900

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Concepts: Kalman filter, Control theory (sociology), Extended Kalman filter, Moving horizon estimation, Estimation, Fast Kalman filter, Computer science, Unscented transform, Estimation theory, Engineering, Artificial intelligence, Algorithm, Control (management), Systems engineering

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Cited by: 18

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abstract_inverted_index.identification	143
abstract_inverted_index.simultaneously	103
cited_by_percentile_year.max	98
cited_by_percentile_year.min	94
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile.value	0.96550025
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True