Neural-Adaptive Finite-Time Consensus Tracking of Singular Multiagent Systems With Actuator Attack
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View abstract on PubMed
Summary
This summary is machine-generated.This study addresses finite-time consensus tracking (FTCT) for singular multiagent systems facing actuator attacks. A novel neural-adaptive protocol ensures system stability and accurate tracking despite complex network threats.
Area Of Science
- Control Theory
- Networked Systems
- Artificial Intelligence
Background
- Actuator attacks pose significant threats to complex networked systems, compromising their stability and performance.
- Singular multiagent systems with time-varying delays present unique challenges in achieving coordinated control.
- Finite-time consensus tracking (FTCT) is crucial for ensuring rapid and stable agreement among agents.
Purpose Of The Study
- To investigate the finite-time consensus tracking (FTCT) control problem for singular multiagent systems under actuator attacks.
- To develop a robust control strategy that can handle time-varying delays and external disturbances.
- To ensure the stability and reliable performance of networked systems despite adversarial actions.
Main Methods
- A state observer is proposed to approximate follower agent states and design sliding mode surfaces under actuator attack.
- A novel neural-adaptive distributed FTCT protocol and actuator attack estimation protocol are designed using radial basis function neural networks.
- Sliding mode control with a partitioning strategy is employed for analyzing FTCT in singular multiagent systems.
Main Results
- Sufficient conditions and solving strategies for FTCT in singular multiagent systems over finite time intervals are provided.
- The proposed neural-adaptive protocol effectively estimates actuator attacks and ensures consensus tracking.
- Simulation examples validate the effectiveness of the proposed FTCT control strategy.
Conclusions
- The developed FTCT control strategy is effective for singular multiagent systems susceptible to actuator attacks.
- The combination of neural networks and sliding mode control offers a robust solution for complex networked systems.
- The study provides a theoretical framework and practical validation for secure and efficient multiagent system control.
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