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Related Experiment Video

Updated: Apr 26, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

Zhouhua Peng, Dan Wang, Hongwei Zhang

IEEE Transactions on Neural Networks and Learning Systems

|July 23, 2014

Summary

This study presents new distributed adaptive controllers for uncertain nonlinear multiagent systems to achieve leader-follower synchronization. The methods ensure synchronization with bounded errors, even without full agent models.

Related Experiment Videos

Last Updated: Apr 26, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

Control Theory
Systems Engineering
Robotics

Background:

Multiagent systems with nonlinear dynamics present synchronization challenges.
Uncertainty in agent models complicates distributed control design.
Leader-follower synchronization is crucial for coordinated multiagent behaviors.

Purpose of the Study:

To develop distributed adaptive synchronization controllers for uncertain nonlinear multiagent systems.
To extend the control design to output feedback scenarios using neighborhood observers.
To ensure robust synchronization with bounded errors in various communication topologies.

Main Methods:

Distributed adaptive control based on neighboring agent states.
Design for both undirected and directed communication topologies.
Extension to output feedback using relative output information and neighborhood observers.
Stability analysis using parameter-dependent Riccati inequalities.
Decoupled observer and controller design for nonlinear systems.

Main Results:

Effective distributed adaptive synchronization controllers are proposed.
The control design does not require accurate agent models.
Output feedback synchronization is achieved with a neighborhood observer.
Stability is proven, guaranteeing bounded residual synchronization errors.
The methods are validated through two illustrative examples.

Conclusions:

The proposed distributed adaptive controllers effectively achieve leader-follower synchronization in uncertain nonlinear multiagent systems.
The observer-based approach enables synchronization under output feedback with decoupled design.
The methods offer a robust solution for practical multiagent coordination problems.