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Related Experiment Video

Updated: May 14, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Fast and Precise Formation of UAV Swarm System via Adaptive Robust Diffeomorphism-Based Constraint-Following Control.

Jiawen Dai, Jiaojiao Liu, Zheshuo Zhang

IEEE Transactions on Cybernetics

|May 12, 2026

Summary

This study introduces an adaptive robust control method for unmanned aerial vehicle (UAV) swarms to achieve fast, precise formation flying while ensuring collision avoidance, even with unknown uncertainties.

Related Experiment Videos

Last Updated: May 14, 2026

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Area of Science:

Robotics
Control Systems Engineering
Aerospace Engineering

Background:

Unmanned Aerial Vehicle (UAV) swarms require advanced control for coordinated maneuvers.
Achieving fast and precise formation (FPF) while ensuring collision avoidance is challenging due to uncertainties.
Existing methods often struggle with simultaneous FPF and collision avoidance under dynamic conditions.

Purpose of the Study:

To develop an adaptive robust diffeomorphism-based constraint-following control (ARDCFC) method for UAV swarms.
To enable fast and precise formation (FPF) with guaranteed collision avoidance.
To address transient and steady-state performance requirements under time-varying uncertainties with unknown bounds.

Main Methods:

Formulating FPF and collision avoidance as inequality constraints.
Transforming inequality and equality constraints into unified equality constraints using a diffeomorphism approach.
Adaptively estimating and utilizing conservative bounds for uncertainties.
Implementing ARDCFC as a constraint-following problem.

Main Results:

The proposed ARDCFC method successfully achieves fast and precise formation (FPF) in UAV swarms.
Simultaneous collision avoidance is guaranteed even with unknown, time-varying uncertainties.
Rigorous proofs and simulations demonstrate the robustness and effectiveness of the control strategy.

Conclusions:

This work presents a novel ARDCFC method for UAV swarm formation control.
It is likely the first study to simultaneously guarantee FPF and collision avoidance for uncertain UAV swarm systems.
The findings offer a significant advancement in autonomous multi-UAV coordination.