Trajectory Tracking on Uncertain Complex Networks via NN-Based Inverse Optimal Pinning Control

Area of Science:

• Complex Networks
• Control Theory
• Neural Networks

Background:

• Trajectory tracking in uncertain complex networks presents significant challenges.
• Existing control methods may struggle with the inherent complexity and uncertainty of these systems.
• The need for robust and efficient control strategies is paramount for network stability and performance.

Purpose of the Study:

• To propose a new control approach for trajectory tracking in uncertain complex networks.
• To implement a neural controller on a subset of network nodes (pinned nodes).
• To ensure and analyze the stability of the proposed control scheme.

Main Methods:

• A neural controller is designed, comprising an on-line identifier utilizing a recurrent high-order neural network.
• An inverse optimal controller is integrated for precise trajectory tracking.
• A stability analysis is performed to validate the control scheme's robustness.

Main Results:

• The proposed neural controller successfully achieves trajectory tracking on uncertain complex networks.
• Simulations using a network of chaotic Lorenz oscillators demonstrate the control scheme's applicability and effectiveness.
• The pinning strategy on a fraction of nodes proves efficient for controlling the entire network's dynamics.

Conclusions:

• The developed neural control approach offers a viable solution for trajectory tracking in complex, uncertain network environments.
• The combination of on-line identification and inverse optimal control ensures reliable performance.
• The study validates the effectiveness of controlling complex networks via targeted node manipulation.