Distributed Control-Estimation Synthesis for Stochastic Multi-Agent Systems via Virtual Interaction between Non-neighboring Agents

TL;DR

This paper introduces a novel distributed control and estimation framework for stochastic multi-agent systems, enabling agents to interact virtually beyond their neighbors, thereby improving control optimality.

Contribution

It proposes a control-estimation synthesis method that relaxes local information constraints by incorporating virtual interactions through distributed estimation.

Findings

Achieves optimal control by expanding interaction scope.

Develops an iterative algorithm for control-estimation law synthesis.

Demonstrates improved control performance in stochastic MAS.

Abstract

This paper considers the optimal distributed control problem for a linear stochastic multi-agent system (MAS). Due to the distributed nature of MAS network, the information available to an individual agent is limited to its vicinity. From the entire MAS aspect, this imposes the structural constraint on the control law, making the optimal control law computationally intractable. This paper attempts to relax such a structural constraint by expanding the neighboring information for each agent to the entire MAS, enabled by the distributed estimation algorithm embedded in each agent. By exploiting the estimated information, each agent is not limited to interact with its neighborhood but further establishing the `virtual interactions' with the non-neighboring agents. Then the optimal distributed MAS control problem is cast as a synthesized control-estimation problem. An iterative optimization procedure is developed to find the control-estimation law, minimizing the global objective cost of MAS.