Vision-Based Adaptive Prescribed-Time Control of UAV for Uncooperative Target Tracking with Performance Constraint
This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle (UAV) under the performance constraint and scaled relative velocity constraint, in which the states of the uncooperative target can only be estimated through a vision sensor. Considering the limite...
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Published in | Journal of systems science and complexity Vol. 37; no. 5; pp. 1956 - 1977 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2024
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | This paper discusses the uncooperative target tracking control problem for the unmanned aerial vehicle (UAV) under the performance constraint and scaled relative velocity constraint, in which the states of the uncooperative target can only be estimated through a vision sensor. Considering the limited detection range, a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking system. Meanwhile, the scaled relative velocity constraint in the dynamic phase is taken into account, and a time-varying nonlinear transformation is used to solve the constraint problem, which not only overcomes the feasibility condition but also fails to violate the constraint boundaries. Finally, the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are bounded. It is proved that the UAV can follow the uncooperative target at the desired relative position within a prescribed time, thereby improving the applicability of the vision-based tracking approach. Simulation results have been presented to prove the validity of the proposed control strategy. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 1009-6124 1559-7067 |
DOI: | 10.1007/s11424-024-3443-2 |