Scalable δ-Level Coherent State Synchronization of Multi-Agent Systems with Adaptive Protocols and Bounded Disturbances

Core Concepts

The authors propose a scale-free framework for achieving δ-level coherent state synchronization of multi-agent systems in the presence of bounded disturbances/noises, without requiring any prior knowledge about the network or the size of the disturbances.

Abstract

The paper studies the problem of scalable δ-level coherent state synchronization for multi-agent systems (MAS) subject to bounded disturbances/noises. The key contributions are: The proposed protocols are designed solely based on the knowledge of the agent models, without any information about the communication network such as bounds on the spectrum of the associated Laplacian matrix or the number of agents. The protocols are scale-free and work for any connected communication network. The authors achieve scalable δ-level coherent state synchronization, where for any given δ, the level of coherency of the network can be restricted to δ. The only assumption is that the disturbances are bounded, but the protocol is independent of the bound and does not require any other knowledge about the disturbances. The paper first introduces the problem formulation and the necessary preliminaries on graph theory. It then presents the protocol design in three steps: 1) finding the matrix P, 2) choosing the parameter d, and 3) obtaining the adaptive protocol. The authors prove that the proposed protocol solves the δ-level coherent state synchronization problem in the presence of bounded disturbances/noises for any connected communication network.

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Scalable δ-Level Coherent State Synchronization of Multi-Agent Systems in the Presence of Bounded Disturbances

by Donya Nojava... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2308.11959.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex agent dynamics, such as nonlinear or time-varying systems

The proposed framework for scalable δ-level coherent state synchronization can be extended to handle more complex agent dynamics by incorporating nonlinear or time-varying systems. One approach is to design adaptive protocols that can accommodate the nonlinear dynamics of the agents. This can involve using techniques from nonlinear control theory, such as feedback linearization or sliding mode control, to ensure stability and synchronization in the presence of nonlinear dynamics. Additionally, the framework can be extended to handle time-varying systems by incorporating time-varying parameters or adaptive control strategies that can adapt to changes in the system dynamics over time. By incorporating these advanced control techniques, the framework can effectively handle a wider range of complex agent dynamics in multi-agent systems.

What are the potential limitations or drawbacks of the δ-level coherent synchronization approach compared to other synchronization techniques like H∞ or H2 almost synchronization

While the δ-level coherent synchronization approach offers scalability and robustness in the presence of disturbances without prior knowledge of their size, there are potential limitations compared to other synchronization techniques like H∞ or H2 almost synchronization. One limitation is the requirement for a tuning parameter δ in the δ-level coherent synchronization approach, which may need to be adjusted based on the specific network characteristics. This tuning requirement can make the approach more sensitive to changes in the communication graph or network size compared to H∞ or H2 techniques, which may offer more robust performance without the need for manual tuning. Additionally, the δ-level coherent synchronization approach may have limitations in handling certain types of disturbances or noise characteristics compared to the more sophisticated techniques like H∞ or H2 almost synchronization, which are designed to address a wider range of disturbance scenarios.

What are the potential real-world applications of the scalable δ-level coherent state synchronization in multi-agent systems, and how could it impact the design and control of cooperative systems like robot networks, autonomous vehicles, or distributed sensor networks

The scalable δ-level coherent state synchronization in multi-agent systems has potential real-world applications in various fields where cooperative control is essential. One application is in robot networks, where multiple robots need to synchronize their movements or behaviors to achieve a common goal. By implementing the δ-level coherent synchronization approach, robot networks can achieve robust and scalable synchronization even in the presence of disturbances or uncertainties. Another application is in autonomous vehicles, where coordination among vehicles is crucial for safe and efficient operation. The δ-level coherent synchronization can ensure that autonomous vehicles maintain synchronization in dynamic environments without the need for detailed knowledge of disturbances. In distributed sensor networks, the approach can be used to synchronize sensor data for accurate and reliable information fusion. Overall, the scalable δ-level coherent state synchronization can significantly impact the design and control of cooperative systems by providing a flexible and robust synchronization framework for multi-agent systems.