Distributed Quantized Feedback Design Strategy for Adaptive Consensus Tracking of Uncertain Strict-Feedback Nonlinear...

Area of Science:

• Control Systems Engineering
• Nonlinear Systems Theory
• Multiagent Systems

Background:

• Distributed adaptive consensus is crucial for multiagent systems.
• Uncertainties and quantized communication pose significant challenges.
• Existing methods often struggle with heterogeneous nonlinearities and limited state information.

Purpose of the Study:

• To develop a quantized feedback control strategy for leader-following consensus in uncertain nonlinear multiagent systems.
• To address the challenge of unknown nonlinearities and heterogeneous system dynamics.
• To enable consensus tracking using only quantized state information under a directed network.

Main Methods:

• A novel adaptive control law is designed based on quantized states.
• Neural network-based function approximators are employed to handle unknown nonlinearities.
• Quantized-signals-based weight tuning laws are developed for the approximators.
• Analysis of quantization error boundedness ensures system stability.

Main Results:

• The proposed control law guarantees uniform ultimate boundedness of all closed-loop signals.
• Consensus tracking errors converge to a small, bounded region around the origin.
• The effectiveness is validated through simulations, including complex systems like ship steering.

Conclusions:

• The study successfully integrates quantized feedback into adaptive leader-following consensus for uncertain nonlinear multiagent systems.
• The developed approach overcomes limitations of prior methods by handling unknown dynamics and quantized communication.
• The findings offer a robust solution for practical distributed control applications.