Neuro-adaptive augmented distributed nonlinear dynamic inversion for consensus of nonlinear agents with unknown...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Multi-agent systems require robust control strategies for coordinated behavior.
• Nonlinear systems present significant challenges due to their complex dynamics.
• Unknown external disturbances can destabilize system consensus.

Purpose of the Study:

• To develop a novel control technique for achieving consensus in nonlinear multi-agent systems.
• To address the challenge of unknown external disturbances in multi-agent coordination.
• To integrate neural networks with distributed nonlinear dynamic inversion for enhanced control.

Main Methods:

• A neuro-adaptive augmented distributed nonlinear dynamic inversion (N-DNDI) controller is proposed.
• The N-DNDI controller combines neural networks with distributed nonlinear dynamic inversion (DNDI).
• The theoretical underpinnings and mathematical framework of the N-DNDI controller are detailed.

Main Results:

• The proposed N-DNDI controller effectively achieves consensus in nonlinear multi-agent systems.
• The controller demonstrates robustness against unknown external disturbances.
• Simulation results validate the efficacy of the N-DNDI control scheme.

Conclusions:

• The N-DNDI controller offers a unique and effective solution for multi-agent consensus problems.
• The integration of neural networks with NDI provides a powerful approach for handling complex dynamics and disturbances.
• The study establishes a strong theoretical foundation and practical validation for the proposed control strategy.