Optimal Output Synchronization of Nonlinear Multi-Agent Systems using Approximate Dynamic Programming

Abstract

Optimal output synchronization of multi-agent leader-follower systems is considered. The agents are assumed heterogeneous so that the dynamics may be non-identical. An optimal control protocol is designed for each agent based on the leader state and the agent local state. A distributed observer is designed to provide the leader state for each agent. A model-free approximate dynamic programming algorithm is then developed to solve the optimal output synchronization problem online in real time. No knowledge of the agents' dynamics is required. The proposed approach does not require explicitly solving of the output regulator equations, though it implicitly solves them by imposing optimality. A simulation example verifies the suitability of the proposed approach.

Meeting Name

2016 International Joint Conference on Neural Networks (IJCNN) (2016: Jul. 24-29, Vancouver, Canada)

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Learning Algorithms; Optimal Control Systems; Reinforcement Learning; Synchronization; Adaptive Observer; Distributed Approaches; Heterogeneous Systems; Leader-Follower; Optimal Control Problem; Output Regulation; Output Synchronization; Regulator Equations; Multi Agent Systems; Leader-Follower Systems

International Standard Book Number (ISBN)

978-1509006199

International Standard Serial Number (ISSN)

2161-4407

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2016

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