Practical Time-Varying Formation Tracking for Second-Order Nonlinear Multiagent Systems With Multiple Leaders Using...

Area of Science:

• Control Theory
• Robotics
• Artificial Intelligence

Background:

• Investigates practical time-varying formation tracking for second-order nonlinear multiagent systems.
• Addresses challenges including multiple leaders, unknown control inputs, and heterogeneous nonlinearities/disturbances.
• Builds upon existing research by incorporating predefined time-varying formations and follower states.

Purpose of the Study:

• To develop an adaptive neural network (NN) based control protocol for robust time-varying formation tracking.
• To handle complex system dynamics with matched/mismatched nonlinearities and disturbances.
• To ensure arbitrarily small formation tracking errors in the presence of uncertainties.

Main Methods:

• Proposes a practical time-varying formation tracking protocol utilizing adaptive neural networks (NNs).
• Employs local neighboring information for control protocol construction.
• Introduces a three-step algorithm for protocol design and feasibility analysis.
• Applies Lyapunov theory to prove closed-loop system stability.

Main Results:

• Achieves arbitrarily small time-varying formation tracking errors.
• Successfully manages matched/mismatched heterogeneous nonlinearities and disturbances.
• Handles unknown control inputs of leaders effectively.
• Demonstrates the protocol's effectiveness through a simulation example.

Conclusions:

• The proposed adaptive NN control protocol ensures practical time-varying formation tracking for complex multiagent systems.
• The method is robust to unknown control inputs, nonlinearities, and external disturbances.
• Lyapunov stability analysis confirms the system's reliability.