Distributed Fault-Tolerant Control of Multiagent Systems: An Adaptive Learning Approach

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Multiagent systems (MAS) are increasingly complex, requiring robust control strategies.
• Faults in distributed agents can compromise system stability and cooperative performance.
• Existing control schemes often struggle with nonlinear dynamics and unknown fault functions.

Purpose of the Study:

• To develop a distributed leader-following fault-tolerant tracking control scheme for high-order nonlinear uncertain MAS.
• To address multiple simultaneous process and actuator faults in distributed agents.
• To ensure system stability and cooperative tracking performance under adverse conditions.

Main Methods:

• Utilizing neural network-based adaptive learning algorithms to identify and compensate for unknown fault functions.
• Implementing a distributed control architecture with directed communication links from leader to followers.
• Designing adaptive fault-tolerant algorithms for both full-state and limited output measurement scenarios.

Main Results:

• Guaranteed system stability and asymptotic leader-follower tracking properties are rigorously established.
• The proposed scheme effectively handles multiple simultaneous process and actuator faults.
• Demonstrated robustness in cooperative tracking for nonlinear uncertain multiagent systems.

Conclusions:

• The developed distributed leader-following fault-tolerant tracking control scheme is effective for high-order nonlinear uncertain MAS.
• Neural network-based adaptive learning provides a robust solution for unknown fault compensation.
• The findings contribute to the advancement of reliable control strategies for complex multiagent systems.