Adaptive Distributed Cooperative Tracking Control and Application for Multiagent Formation Under Communication Constraints

This article studies the problem of distributed adaptive cooperative tracking for multiagent systems with communication constraints in complex communication environments. For this problem, a novel quantitative adaptive tracking control strategy is proposed based on adaptive backstepping technology,...

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Veröffentlicht in:IEEE transactions on aerospace and electronic systems Jg. 60; H. 4; S. 4492 - 4506
Hauptverfasser: Zhang, Jialong, Ye, Lei, Hou, Zhongxi, Yu, Li, Cai, Jia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:0018-9251, 1557-9603
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Abstract This article studies the problem of distributed adaptive cooperative tracking for multiagent systems with communication constraints in complex communication environments. For this problem, a novel quantitative adaptive tracking control strategy is proposed based on adaptive backstepping technology, quantization theory, and variable transformation. First, the mechanism of communication between agents or between agents and ground stations is analyzed and quantized. And a multiagent swarm formation model with quantized inputs is established by considering a class of high-order nonlinear multiagent systems with partially unknown parameters. Moreover, the limitation of the nonholonomic constraint is removed via the variable transformation method. The adaptive backstepping method is used to design an adaptive cooperative tracking controller to update the unknown parameters of the model in real-time. Formation flight is maintained by designing time-varying reference trajectories to track the desired specified positions. Then, the rationality of the proposed method is verified in terms of Lyapunov stability. Finally, the designed control strategy is applied to a scaled-down model for an unmanned aerial vehicle formation to further verify its effectiveness in practical engineering applications.
AbstractList This article studies the problem of distributed adaptive cooperative tracking for multiagent systems with communication constraints in complex communication environments. For this problem, a novel quantitative adaptive tracking control strategy is proposed based on adaptive backstepping technology, quantization theory, and variable transformation. First, the mechanism of communication between agents or between agents and ground stations is analyzed and quantized. And a multiagent swarm formation model with quantized inputs is established by considering a class of high-order nonlinear multiagent systems with partially unknown parameters. Moreover, the limitation of the nonholonomic constraint is removed via the variable transformation method. The adaptive backstepping method is used to design an adaptive cooperative tracking controller to update the unknown parameters of the model in real-time. Formation flight is maintained by designing time-varying reference trajectories to track the desired specified positions. Then, the rationality of the proposed method is verified in terms of Lyapunov stability. Finally, the designed control strategy is applied to a scaled-down model for an unmanned aerial vehicle formation to further verify its effectiveness in practical engineering applications.
Author Cai, Jia
Yu, Li
Hou, Zhongxi
Zhang, Jialong
Ye, Lei
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SubjectTerms Adaptive back-stepping technology
Adaptive control
Adaptive systems
Autonomous aerial vehicles
Communication
Communication networks
Constraints
Control systems design
Cooperative control
cooperative tracking control
Design parameters
Formation flying
Ground stations
Hysteresis
input quantization
Multiagent systems
Nonlinear systems
Quantization (signal)
Real time
swarm formation
Target tracking
time-varying trajectory
Tracking control
Trajectory
Trajectory control
Unmanned aerial vehicles
Title Adaptive Distributed Cooperative Tracking Control and Application for Multiagent Formation Under Communication Constraints
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