The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles
Abstract
This paper presents a preview steering control algorithm and its closed-loop system analysis and experimental validation for accurate, smooth, and computationally inexpensive path tracking of automated vehicles. The path tracking issue is formulated as an optimal control problem with dynamic disturbance, i.e., the future road curvature. A discrete-time preview controller is then designed on the top of a linear augmented error system, in which the disturbances within a finite preview window are augmented as part of the state vector. The obtained optimal steering control law is in an analytic form and consists of two parts: 1) a feedback control responding to tracking errors and 2) a feedforward control dealing with the future road curvatures. The designed control's nature, capacity, computation load, and underlying mechanism are revealed by the analysis of system responses in the time domain and the frequency domain, theoretical steady-state error, and comparison with the model predictive control (MPC). The algorithm was implemented on an automated vehicle platform, a hybrid Lincoln MKZ. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy, steering smoothness, and computational efficiency compared to the MPC and the full-state feedback control.
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Cites
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A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B., Cap, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,585
Model predictive control: Recent developments and future promise
Mayne, D. Q. (2014) · Automatica · doi:10.1016/j.automatica.2014.10.128 · Cited by 1,975
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Optimal control: Linear quadratic methods
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Predictive Active Steering Control for Autonomous Vehicle Systems
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(2013) · Dynamic Modelling and Predictive Control in Solid Oxide Fuel Cells: First Principle and Data‐Based Approaches · doi:10.1002/9781118501054.ch6 · Cited by 1,047
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Cited by
Path Tracking Control for Autonomous Vehicles Based on an Improved MPC
Wang, H., Liu, B., Ping, X., & An, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2944894 · Cited by 264
Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges
Yao, Q., Tian, Y., Wang, Q., & Wang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3020075 · Cited by 211
Preview Path Tracking Control With Delay Compensation for Autonomous Vehicles
Xu, S., Peng, H., & Tang, Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2978417 · Cited by 165
Self-citationImplications of automated vehicles for physical road environment: A comprehensive review
Tengilimoglu, O., Carsten, O., & Wadud, Z. (2023) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2022.102989 · Cited by 149
Metadata
- Authors
- Xu, S., & Peng, H.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-01
- Volume
- 21
- Issue
- 1
- Pages
- 48-58
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2913416895
- Cited by
- 386
- References
- 24
- FWCI
- 16.83
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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