Neuro-Adaptive Consensus Tracking of Multiagent Systems With a High-Dimensional Leader

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Multi-agent systems often face challenges with uncertain dynamics and external disturbances.
• Existing consensus tracking methods may not handle differing leader-follower dimensions or unmodeled dynamics effectively.

Purpose of the Study:

• To develop a distributed robust adaptive neural network controller for uncertain multi-agent systems.
• To achieve consensus tracking for systems with a single high-dimensional leader, even with unmodeled dynamics and external disturbances.
• To extend the consensus tracking approach to switching directed communication topologies.

Main Methods:

• Design of a distributed robust adaptive neural network controller for each follower.
• Integration of a local observer to estimate leader states.
• Utilization of multiple Lyapunov functions for stability analysis.
• Extension of methods to handle switching directed communication topologies.

Main Results:

• Achieved synchronization of follower states to the leader's output with bounded residual errors.
• Demonstrated robustness against unmodeled dynamics and external disturbances.
• Successfully extended the consensus tracking to switching topologies.
• Validated the controller's effectiveness through numerical simulations.

Conclusions:

• The proposed distributed robust adaptive neural network controller effectively enables consensus tracking in uncertain multi-agent systems.
• The framework accommodates practical challenges like unmodeled dynamics, external disturbances, and differing leader-follower dimensions.
• The approach is extendable to dynamic network topologies, enhancing its applicability.