Longitudinal velocity control of autonomous driving based on extended state observer

Abstract Active Disturbance Rejection Control (ADRC) possesses robust disturbance rejection capabilities, making it well‐suited for longitudinal velocity control. However, the conventional Extended State Observer (ESO) in ADRC fails to fully exploit feedback from first‐order and higher‐order estimation errors and tracking error simultaneously, thereby diminishing the control performance of ADRC. To address this limitation, an enhanced car‐following algorithm utilising ADRC is proposed, which integrates the improved ESO with a feedback controller. In comparison to the conventional ESO, the enhanced version effectively utilises multi‐order estimation and tracking errors. Specifically, it enhances convergence rates by incorporating feedback from higher‐order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error feedback. The improved ESO significantly enhances the disturbance rejection performance of ADRC. Finally, the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

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Publication Details

Journal
CAAI Transactions on Intelligence Technology
Published
2025-01-10
DOI
https://doi.org/10.1049/cit2.12397
Citations
4
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
2.73

Funders

Controls
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Longitudinal velocity control of autonomous driving based on extended state observer

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Longitudinal velocity control of autonomous driving based on extended state observer

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article en
4 citations

Abstract

Abstract Active Disturbance Rejection Control (ADRC) possesses robust disturbance rejection capabilities, making it well‐suited for longitudinal velocity control. However, the conventional Extended State Observer (ESO) in ADRC fails to fully exploit feedback from first‐order and higher‐order estimation errors and tracking error simultaneously, thereby diminishing the control performance of ADRC. To address this limitation, an enhanced car‐following algorithm utilising ADRC is proposed, which integrates the improved ESO with a feedback controller. In comparison to the conventional ESO, the enhanced version effectively utilises multi‐order estimation and tracking errors. Specifically, it enhances convergence rates by incorporating feedback from higher‐order estimation errors and ensures the estimated value converges to the reference value by utilising tracking error feedback. The improved ESO significantly enhances the disturbance rejection performance of ADRC. Finally, the effectiveness of the proposed algorithm is validated through the Lyapunov approach and experiments.

CAAI Transactions on Intelligence TechnologyVol. 10(1)
Jiangsu University (CN), University of Science and Technology of China (CN), Nanyang Technological University (SG), Beijing Jiaotong University (CN), Hubei University of Automotive Technology (CN), Chery Automobile (China) (CN), State Key Laboratory of Vehicle NVH and Safety Technology (CN), Tsinghua University (CN)
National Natural Science Foundation of China, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body
Openalex Percentile: Top 12%
Vehicle Dynamics and Control Systems
2.73
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