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The HoneyComb Paradigm for Research on Collective Human Behavior

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Leader-Following Consensus for High-Order Nonlinear Stochastic Multiagent Systems.

Changchun Hua, Yafeng Li, Xinping Guan

IEEE Transactions on Cybernetics

|January 28, 2017

Summary

This summary is machine-generated.

This study presents new nonlinear distributed controllers for high-order stochastic multiagent systems, enabling consensus tracking even with uncertain nonlinearities and varying agent orders. The method simplifies implementation for broader applications.

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Area of Science:

Control Theory
Systems Engineering
Robotics

Background:

Distributed consensus tracking is crucial for coordinated multiagent systems.
High-order stochastic systems with uncertain nonlinearities present significant control challenges.
Existing methods often require agents to have identical orders, limiting applicability.

Purpose of the Study:

To develop novel nonlinear distributed controllers for high-order stochastic multiagent systems.
To address consensus tracking problems under fixed undirected graphs with uncertain nonlinear functions.
To overcome limitations of existing methods by accommodating agents with different orders.

Main Methods:

A recursive design approach is employed to create nonlinear distributed controllers.
A specialized virtual controller form is utilized to decouple agent state variables, except for adjacent outputs.
The controller design relies on local agent states and outputs from neighboring agents.

Main Results:

Novel nonlinear distributed controllers are successfully designed for the specified multiagent systems.
The controllers ensure consensus tracking despite uncertain nonlinearities and varying agent orders.
Agent state variables are effectively separated, simplifying controller implementation.

Conclusions:

The proposed method offers a more practical and widely applicable solution for distributed consensus tracking.
The controller's independence from identical agent orders enhances its implementability.
Simulation results validate the efficiency and effectiveness of the developed control strategy.