Consensus Control of Nonlinear Multiagent Systems With Time-Varying State Constraints.

Area of Science:

• Control Theory
• Robotics
• Networked Systems

Background:

• Multiagent systems often face challenges with state constraints.
• Existing consensus algorithms may not handle time-varying or asymmetric constraints effectively.
• Ensuring bounded states is crucial for safety and performance in networked systems.

Purpose of the Study:

• To develop a novel adaptive consensus algorithm for nonlinear multiagent systems.
• To guarantee that agent states remain within user-defined, time-varying asymmetric constraints.
• To advance consensus stabilization beyond traditional methods by incorporating bounded state control.

Main Methods:

• Transformation of the original multiagent system into a new, manageable system.
• Utilizing a single online-tuned neural network (NN) for approximating unknown agent dynamics.
• Incorporating a robust term to handle NN approximation errors, reconstruction inaccuracies, and external disturbances.
• Employing Lyapunov synthesis for theoretical proof of stability and consensus.

Main Results:

• Demonstrated that consensus of transformed states ensures consensus of original agents within constraints.
• The proposed algorithm is decentralized, with agents only communicating with neighbors.
• Successfully approximated unknown dynamics and managed approximation errors using NN and robust terms.
• Validated the algorithm's performance through simulations on a nonlinear multiagent system.

Conclusions:

• The novel adaptive consensus algorithm effectively achieves bounded state control in nonlinear multiagent systems.
• The method provides a robust and decentralized solution for systems with time-varying asymmetric constraints.
• The findings contribute to the advancement of control strategies for complex networked systems.