Neuroadaptive consensus learning for multi-agent systems: An incremental approach to nonstrict pure-feedback control.

Area of Science:

• Control Engineering
• Artificial Intelligence
• Systems Science

Background:

• Multi-agent systems (MAS) present challenges in distributed learning and consensus control.
• Non-strict pure-feedback structures complicate controller design due to inherent algebraic loops.
• Existing methods often struggle with computational complexity and robustness.

Purpose of the Study:

• To develop a unified adaptive learning consensus control framework for non-strict pure-feedback MAS.
• To address and resolve the algebraic loop problem using neural network approximations.
• To enhance control scheme robustness and reduce computational overhead.

Main Methods:

• Integration of backstepping techniques with neural network approximation for a unified framework.
• Development of a neural network-based solution to circumvent algebraic loop issues.
• Implementation of an incremental adaptive mechanism for efficient parameter updating and reduced complexity.

Main Results:

• The proposed control scheme effectively simplifies controller architecture.
• Incremental adaptation significantly reduces computational overhead.
• Theoretical analysis confirms prescribed tracking performance and uniform boundedness of closed-loop signals.

Conclusions:

• The developed distributed learning consensus control is effective for non-strict pure-feedback MAS.
• The neural network-based approach offers a robust and computationally efficient solution.
• Numerical simulations validate the algorithm's performance and learning capabilities.