Predefined-time consensus control for multiagent systems with input and output quantization.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Nonlinear multi-agent systems (NMASs) present significant control challenges.
• Existing predefined-time control methods often lack adaptability and robust handling of quantized communication.

Purpose of the Study:

• To develop a novel adaptive neural consensus control strategy for unknown NMASs operating under predefined-time constraints.
• To address the limitations of existing methods by incorporating quantized communication and directed network topology.

Main Methods:

• Utilizing neural networks for approximating unknown system functions.
• Designing a neural-network-based distributed state observer for unmeasurable states.
• Employing backstepping and command filtering to manage virtual control signal derivatives with quantized outputs.
• Developing a novel Lemma 12 for predefined-time stability analysis with quantization error compensation.

Main Results:

• The proposed strategy successfully achieves consensus, with follower outputs converging to a neighborhood of the leader's output.
• All signals within the closed-loop system are proven to remain bounded.
• The control strategy is effective within a predefined time frame.

Conclusions:

• The novel adaptive neural consensus control strategy offers a robust solution for unknown NMASs with quantized communication.
• The integration of neural networks, state observers, and advanced control techniques enables predefined-time stability and bounded system signals.