Cooperative tracking control of nonlinear multiagent systems using self-structuring neural networks

Area of Science:

• Control Systems Engineering
• Robotics
• Artificial Intelligence

Background:

• Cooperative tracking is crucial for multiagent systems, enabling coordinated behavior.
• Existing methods struggle with nonlinear systems, unknown dynamics, and limited communication.
• Leader-follower architectures are common but require robust control under uncertainty.

Purpose of the Study:

• To develop a cooperative dynamic observer for nonlinear multiagent systems with unknown dynamics.
• To design an observer-based cooperative controller for leader-following tasks under directed graphs.
• To ensure tracking performance despite local interactions and leader accessibility limitations.

Main Methods:

• Utilized a self-structuring neural network (NN) to approximate unknown system dynamics.
• Introduced a cooperative dynamic observer at each agent node.
• Employed graph theory, Lyapunov stability analysis, and a separation principle for controller design.

Main Results:

• Demonstrated that follower agents can track the leader if the communication graph contains a spanning tree.
• The proposed framework effectively handles systems with different, unknown dynamics.
• Simulation results on networked robots validate the control algorithm's effectiveness.

Conclusions:

• The observer-based cooperative controller ensures successful tracking in complex multiagent systems.
• The approach overcomes limitations of traditional tracking control strategies in uncertain environments.
• Network topology, specifically a spanning tree, is essential for achieving cooperative tracking.