Optimal Synchronization Control for Heterogeneous Multi-Agent Systems: Online Adaptive Learning Solutions
Yuanqiang Zhou, Dewei Li, and Furong Gao
TL;DR
This paper introduces an online adaptive learning method for optimal synchronization control in heterogeneous multi-agent systems, utilizing a novel distributed policy iteration approach to improve coordination efficiency.
Contribution
It proposes a new distributed policy iteration algorithm for online adaptive control of heterogeneous multi-agent systems, addressing synchronization challenges.
Findings
Effective synchronization achieved in heterogeneous systems
Improved control performance through adaptive learning
Distributed approach reduces computational complexity
Abstract
This paper presents an online adaptive learning solution to optimal synchronization control problem of heterogeneous multi-agent systems via a novel distributed policy iteration approach.
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Adaptive Dynamic Programming Control · Advanced Memory and Neural Computing