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Impulsive Communication With Full and Partial Information for Adaptive Tracking Consensus of Uncertain Second-Order
IEEE Transactions on Cybernetics
|April 20, 2021
Summary
This study addresses adaptive tracking consensus in uncertain multi-agent systems using impulsive communication. Neural network control schemes ensure estimation and convergence despite system uncertainties and partial information availability.
Area of Science:
- Control Engineering
- Networked Systems
- Artificial Intelligence
Background:
- Uncertainty in dynamics is common in networked control systems.
- Reducing energy costs is a key engineering challenge.
- Achieving consensus in multi-agent systems is crucial for coordinated behavior.
Purpose of the Study:
- To investigate the adaptive tracking consensus problem for uncertain second-order multi-agent systems.
- To develop control schemes using neural networks for systems with impulsive communication.
- To address scenarios with full and partial state information availability.
Main Methods:
- Designing adaptive control schemes incorporating neural networks.
- Developing estimators for followers that operate without neighbor information between communication instants.
- Utilizing impulsive communication for information exchange at discrete time points.
Main Results:
- Sufficient conditions for estimation and convergence were derived for both full and partial information cases.
- The proposed adaptive schemes demonstrate effectiveness in achieving tracking consensus.
- Acknowledged inherent errors in estimation and consensus due to system uncertainties and adaptive control.
Conclusions:
- The developed adaptive control strategies are effective for uncertain second-order multi-agent systems with impulsive communication.
- The method provides a viable approach for achieving tracking consensus under challenging conditions.
- Numerical simulations confirm the practical applicability and effectiveness of the proposed techniques.
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