Data-Driven Synchronization Control for Homogeneous and Heterogeneous Multiagent Systems

Temel Kavramlar

This paper presents novel data-driven solutions to the synchronization problem in both homogeneous and heterogeneous multiagent systems, without requiring knowledge of the agents' dynamics.

Özet

The paper addresses the synchronization problem in multiagent systems, where a group of dynamical agents must coordinate their states to achieve a common value. Two main settings are considered:
Homogeneous Agents:

The agents have identical linear dynamics, but limited sensing capabilities described by a communication graph.
A data-based representation of the synchronization error dynamics is obtained by collecting persistent excitation (PE) input-state data from one of the agents.
An LMI-based procedure is proposed to design distributed static controllers that stabilize the synchronization errors, without requiring any model knowledge.
Heterogeneous Agents:

The agents have different linear dynamics, and a leader agent with known dynamics is present.
Data-based representations of the individual agent and leader trajectories are obtained by collecting PE input-state-output data from each agent.
A data-driven version of a dynamic controller is designed to achieve output synchronization with the leader, without requiring any knowledge of the agents' or leader's models.
The theoretical results are validated through numerical simulations.

İstatistikler

The paper does not provide any specific numerical data or metrics to support the key arguments. The focus is on the theoretical development of the data-driven synchronization control methods.

Alıntılar

"This paper presents novel solutions of the data-based synchronization problem for continuous-time multiagent systems."
"Different from existing results, we do not require model knowledge for the followers and the leader."

Önemli Bilgiler Şuradan Elde Edildi

Data-Based System Representation and Synchronization for Multiagent Systems

by Vict... : arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13937.pdf

Daha Derin Sorular

How can the proposed data-driven methods be extended to handle uncertainties or disturbances in the multiagent system dynamics

The proposed data-driven methods can be extended to handle uncertainties or disturbances in the multiagent system dynamics by incorporating robust control techniques. One approach is to utilize robust control strategies such as H-infinity control or sliding mode control to design controllers that can handle uncertainties in the system dynamics. By incorporating robustness considerations into the controller design process, the system can achieve synchronization even in the presence of disturbances or uncertainties. Additionally, adaptive control techniques can be employed to continuously adjust the controller parameters based on the observed data, allowing the system to adapt to changing dynamics and disturbances.

What are the limitations of the current approaches in terms of scalability to large-scale multiagent networks

The current approaches may face limitations in terms of scalability to large-scale multiagent networks due to the computational complexity and communication requirements. As the number of agents in the network increases, the data collection, processing, and control computation become more challenging. The communication overhead between agents can also increase significantly in large-scale networks, leading to delays and potential synchronization issues. Moreover, the storage and processing of large amounts of data from multiple agents can pose challenges in terms of real-time implementation and computational resources. Therefore, scalability issues need to be carefully addressed when applying data-driven synchronization techniques to large-scale multiagent systems.

Can the data-driven synchronization techniques be combined with other distributed optimization or learning-based algorithms to achieve additional performance objectives beyond just synchronization

Data-driven synchronization techniques can be combined with other distributed optimization or learning-based algorithms to achieve additional performance objectives beyond just synchronization. For example, reinforcement learning algorithms can be integrated to optimize the synchronization performance based on the collected data and feedback from the system. By incorporating reinforcement learning, the system can adaptively learn the optimal control policies to improve synchronization efficiency and robustness. Additionally, distributed optimization algorithms can be used to optimize the overall network performance while ensuring synchronization among the agents. By combining data-driven synchronization techniques with optimization and learning-based approaches, the system can achieve multiple objectives such as energy efficiency, resource allocation, and task coordination in a distributed and adaptive manner.