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Neural-network-based adaptive leader-following control for multiagent systems with uncertainties.
Long Cheng1, Zeng-Guang Hou, Min Tan
1Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. chenglong@compsys.ia.ac.cn
IEEE Transactions on Neural Networks
|July 6, 2010
Summary
This study introduces a neural-network adaptive control for multiagent systems, enabling follower agents to precisely track a leader
Area of Science:
- Robotics and Control Systems
- Artificial Intelligence
- Networked Systems
Background:
- Multiagent systems require robust control strategies to manage complex interactions.
- Adaptive control is crucial for systems with uncertain dynamics and external disturbances.
- Leader-following control is a fundamental problem in multiagent coordination.
Purpose of the Study:
- To develop a neural-network-based adaptive control approach for leader-following multiagent systems.
- To address uncertain agent dynamics and external disturbances effectively.
- To enable follower agents to track a time-varying leader state.
Main Methods:
- Utilizing a neural network to approximate uncertain agent dynamics.
- Employing a robust signal to counteract approximation errors and disturbances.
- Designing a decentralized control algorithm dependent only on neighbor information.
Main Results:
- Guaranteed tracking of the leader's time-varying state for all follower agents.
- Achieving arbitrarily small tracking errors without control input constraints.
- Demonstrated robustness against agent dynamics uncertainty and external disturbances.
Conclusions:
- The proposed neural-network adaptive control is effective for leader-following multiagent systems.
- The method accounts for agent dynamics uncertainty and time-varying leader states.
- Decentralized control based on local information is validated through simulations.
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