Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent State-Delay...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Existing adaptive consensus methods using neural networks (NN) or fuzzy logic systems (FLS) face significant computational burdens.
• Multiagent systems often encounter uncertainties from unknown nonlinear dynamics and state delays.

Purpose of the Study:

• To develop a computationally efficient adaptive consensus method for multiagent systems.
• To counteract system uncertainties and compensate for state delays in multiagent agreement control.

Main Methods:

• Employing adaptive neural networks (NN) to counteract unknown nonlinear dynamics.
• Designing a Lyapunov-Krasovskii functional to compensate for state delays.
• Utilizing Lyapunov stability theory to demonstrate consensus achievement.

Main Results:

• The proposed approach significantly alleviates the computation burden by updating only a few adaptive parameters online.
• The consensus scheme successfully steers multiagent systems to synchronize with predefined reference signals.
• Effectiveness verified through simulations on a numerical multiagent system and a practical multimanipulator system.

Conclusions:

• The novel adaptive consensus scheme offers a computationally efficient and effective solution for multiagent synchronization.
• The method robustly handles system uncertainties and state delays, outperforming existing NN/FLS-based approaches.
• Validated effectiveness for both numerical and practical multiagent systems.