Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control

In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design p...
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Autor*in:	Kibeom Lee [verfasserIn] Seungmin Jeon [verfasserIn] Heegwon Kim [verfasserIn] Dongsuk Kum [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Autonomous vehicle intelligent vehicle linear quadratic Gaussian (LQG) control look-ahead distance path tracking

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 109120-109133
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:109120-109133

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2933895

Katalog-ID:	DOAJ066549337

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spelling	10.1109/ACCESS.2019.2933895 doi (DE-627)DOAJ066549337 (DE-599)DOAJ55e5b2ff869c4b64b9da7e9964b1e9c1 DE-627 ger DE-627 rakwb eng TK1-9971 Kibeom Lee verfasserin aut Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods. Autonomous vehicle intelligent vehicle linear quadratic Gaussian (LQG) control look-ahead distance path tracking Electrical engineering. Electronics. Nuclear engineering Seungmin Jeon verfasserin aut Heegwon Kim verfasserin aut Dongsuk Kum verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 109120-109133 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:109120-109133 https://doi.org/10.1109/ACCESS.2019.2933895 kostenfrei https://doaj.org/article/55e5b2ff869c4b64b9da7e9964b1e9c1 kostenfrei https://ieeexplore.ieee.org/document/8792042/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 109120-109133
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allfieldsGer	10.1109/ACCESS.2019.2933895 doi (DE-627)DOAJ066549337 (DE-599)DOAJ55e5b2ff869c4b64b9da7e9964b1e9c1 DE-627 ger DE-627 rakwb eng TK1-9971 Kibeom Lee verfasserin aut Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods. Autonomous vehicle intelligent vehicle linear quadratic Gaussian (LQG) control look-ahead distance path tracking Electrical engineering. Electronics. Nuclear engineering Seungmin Jeon verfasserin aut Heegwon Kim verfasserin aut Dongsuk Kum verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 109120-109133 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:109120-109133 https://doi.org/10.1109/ACCESS.2019.2933895 kostenfrei https://doaj.org/article/55e5b2ff869c4b64b9da7e9964b1e9c1 kostenfrei https://ieeexplore.ieee.org/document/8792042/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 109120-109133
allfieldsSound	10.1109/ACCESS.2019.2933895 doi (DE-627)DOAJ066549337 (DE-599)DOAJ55e5b2ff869c4b64b9da7e9964b1e9c1 DE-627 ger DE-627 rakwb eng TK1-9971 Kibeom Lee verfasserin aut Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods. Autonomous vehicle intelligent vehicle linear quadratic Gaussian (LQG) control look-ahead distance path tracking Electrical engineering. Electronics. Nuclear engineering Seungmin Jeon verfasserin aut Heegwon Kim verfasserin aut Dongsuk Kum verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 109120-109133 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:109120-109133 https://doi.org/10.1109/ACCESS.2019.2933895 kostenfrei https://doaj.org/article/55e5b2ff869c4b64b9da7e9964b1e9c1 kostenfrei https://ieeexplore.ieee.org/document/8792042/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 109120-109133
language	English
source	In IEEE Access 7(2019), Seite 109120-109133 volume:7 year:2019 pages:109120-109133
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topic_facet	Autonomous vehicle intelligent vehicle linear quadratic Gaussian (LQG) control look-ahead distance path tracking Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Kibeom Lee @@aut@@ Seungmin Jeon @@aut@@ Heegwon Kim @@aut@@ Dongsuk Kum @@aut@@
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callnumber-first	T - Technology
author	Kibeom Lee
spellingShingle	Kibeom Lee misc TK1-9971 misc Autonomous vehicle misc intelligent vehicle misc linear quadratic Gaussian (LQG) control misc look-ahead distance misc path tracking misc Electrical engineering. Electronics. Nuclear engineering Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control
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topic_title	TK1-9971 Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control Autonomous vehicle intelligent vehicle linear quadratic Gaussian (LQG) control look-ahead distance path tracking
topic	misc TK1-9971 misc Autonomous vehicle misc intelligent vehicle misc linear quadratic Gaussian (LQG) control misc look-ahead distance misc path tracking misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Autonomous vehicle misc intelligent vehicle misc linear quadratic Gaussian (LQG) control misc look-ahead distance misc path tracking misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Autonomous vehicle misc intelligent vehicle misc linear quadratic Gaussian (LQG) control misc look-ahead distance misc path tracking misc Electrical engineering. Electronics. Nuclear engineering
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title	Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control
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title_full	Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control
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title_auth	Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control
abstract	In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods.
abstractGer	In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods.
abstract_unstemmed	In practice, many autonomous vehicle developers put a tremendous amount of time and efforts in tuning and calibrating the path tracking controllers in order to achieve robust tracking performance and smooth steering actions over a wide range of vehicle speed and road curvature changes. This design process becomes tiresome when the target vehicle changes frequently. In this study, a model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The regulator, in turn, is automatically designed, without additional efforts for tuning at various speeds. The performance of the proposed algorithm is validated based on KAIST autonomous vehicle. The experimental results show that the proposed LQG with adaptive Q-matrix has tracking performance in both low (15kph) and high (45kph) speed driving conditions better than those of other conventional tracking methods like the Stanley and Pure-pursuit methods.
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title_short	Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control
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Optimal Path Tracking Control of Autonomous Vehicle: Adaptive Full-State Linear Quadratic Gaussian (LQG) Control

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