Predefined Accuracy Adaptive Tracking Control for Nonlinear Multiagent Systems With Unmodeled Dynamics.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Nonlinear multiagent systems (MASs) present challenges in control due to unmodeled dynamics and input quantization.
• Achieving precise tracking control in such systems is complex and requires advanced methodologies.

Purpose of the Study:

• To develop an adaptive dynamic surface tracking control strategy for nonlinear MASs.
• To address unmodeled dynamics and input quantization while ensuring predefined accuracy.
• To mitigate the "explosion of complexity" issue common in MAS control.

Main Methods:

• Employing radial basis function neural networks (RBFNNs) for estimating unknown nonlinear functions.
• Introducing a dynamic signal to manage unmodeled dynamics.
• Applying dynamic surface control (DSC) with a nonlinear filter to prevent complexity explosion.
• Utilizing two key functions to achieve predefined precision control.

Main Results:

• The designed controller effectively manages unmodeled dynamics and input quantization.
• Consensus errors in the MAS converge to a predetermined accuracy level.
• Simulation results validate the proposed control strategy's effectiveness.

Conclusions:

• The proposed adaptive dynamic surface control strategy is effective for nonlinear MASs.
• The method successfully achieves predefined accuracy despite system uncertainties and limitations.
• The approach offers a viable solution for complex MAS control problems.