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Distributed finite-time containment control for double-integrator multiagent systems.

Xiangyu Wang, Shihua Li, Peng Shi

IEEE Transactions on Cybernetics

|August 20, 2014

Summary

This summary is machine-generated.

This study introduces finite-time containment control for multiagent systems with multiple leaders and disturbances. It ensures follower agents converge to a dynamic convex hull in finite time, even with external disturbances.

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Area of Science:

Control Theory
Robotics
Distributed Systems

Background:

Multiagent systems require coordinated control for tasks like formation flying and distributed sensing.
Achieving containment control, where followers stay within a region defined by leaders, is crucial for many applications.
Finite-time control offers faster convergence compared to traditional asymptotic control.

Purpose of the Study:

To address the distributed finite-time containment control problem for double-integrator multiagent systems.
To handle systems with multiple dynamic leaders and external disturbances.
To ensure follower states converge to the dynamic convex hull of leader states in finite time.

Main Methods:

Development of a distributed finite-time observer using homogeneous control techniques to estimate leaders' velocities.
Design of distributed finite-time containment control algorithms based on observer estimates and the generalized adding a power integrator approach.
Adaptation of algorithms for stationary leaders as a special case, eliminating the need for a distributed observer.

Main Results:

The proposed observer successfully estimates the weighted average of leaders' velocities in finite time.
The designed control algorithms guarantee finite-time convergence of follower states to the dynamic convex hull of leader states.
The algorithms are effective for both dynamic and stationary multiple leaders, including cases with external disturbances.

Conclusions:

The study presents a robust solution for finite-time containment control in complex multiagent systems.
The developed methods provide fast and stable convergence, enhancing the performance of coordinated robotic systems.
The findings are validated through simulations, demonstrating the practical applicability of the proposed control strategies.