Multiple Vehicle Formation Control Based on Robust Adaptive Control Algorithm
For formation control of multiple vehicle, the typical method is feedback linearization, wherein followers track the leader within a constant distance. Uncertainties and noises arising from varying external environments may easily lead to tracking errors, however. In this paper, the leader-follower error model is built and an adaptive controller is designed to address the uncertain relative distance using the dynamic estimation of the leader-follower distance. Further, a robust adaptive controller is developed to tackle inevitable white noise in the follower velocity detection. Simulation validations demonstrate that the proposed robust adaptive controller performs better than other controllers, given uncertainties and noise. Finally, the multiple vehicles formation mode is extended from two vehicles to five by using graph theory. This paper simulates five vehicles in triangle formation using a robust adaptive controller. The results confirm the viability of the robust adaptive controller under conditions of uncertain relative distances between vehicles, given external noise.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
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Supplemental Notes:
- Copyright © 2017, IEEE.
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Authors:
- Li, Runmei
- Zhang, Liwei
- Han, Lu
- Wang, Jian
- Publication Date: 2017
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 41-51
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Serial:
- IEEE Intelligent Transportation Systems Magazine
- Volume: 9
- Issue Number: 2
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1939-1390
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645
Subject/Index Terms
- TRT Terms: Adaptive control; Automatic headway control; Graph theory; Intelligent vehicles
- Uncontrolled Terms: Robustness
- Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01635648
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 20 2017 2:28PM