Distributed Control of Nonlinear Multiagent Systems with Asymptotic Consensus

Abstract

An adaptive consensus algorithm is proposed for a class of nonlinear multiagent systems with completely unknown agent dynamics. Due to uncertainties in the agent's dynamics, previous consensus approaches usually yield uniformly ultimately bounded consensus error. Our main contribution includes a novel robust consensus algorithm which can guarantee that the consensus error converges to zero asymptotically. In order to address the unknown dynamics, a two-layer neural network (NN) is utilized to learn the unknown dynamics in an online manner, and a robust continuous term is introduced to alleviate effects of the NN residual reconstruction error and external disturbances. The continuousness of the control signal is guaranteed to remove the actuator bandwidth requirement and avoid the caused chattering phenomenon. The proposed consensus algorithm is distributed in the sense that each agent only exchanges information with its neighbor agents. The asymptotic consensus result is achieved via Lyapunov synthesis. Furthermore, the proposed algorithm can also be extended to the case where the agents are required to form a prescribed formation. Finally, simulation studies on a nonlinear multiagent system are provided to demonstrate the performance of the scheme.

Department(s)

Electrical and Computer Engineering

Comments

This work was supported by the National Natural Science Foundation of China under Grant 61673347 and Grant U1609214.

Keywords and Phrases

Distributed parameter control systems; Dynamics; Electric ship equipment; Errors; Multi agent systems; Network layers; Adaptive; consensus; Distributed control; formation; nonlinear; Uncertain; Software agents

International Standard Serial Number (ISSN)

2168-2216; 2168-2232

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2017

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