Batch-Based Learning Consensus of Multiagent Systems With Faded Neighborhood Information

Area of Science:

• Control Engineering
• Networked Systems
• Distributed Computing

Background:

• Multiagent systems (MASs) face challenges in achieving consensus due to unreliable wireless communication, including signal fading and additive noise.
• Ensuring precise consensus tracking to a reference leader with contaminated information is crucial for effective MAS operation.
• Existing methods may not adequately address the impact of random fading and noise on consensus performance.

Purpose of the Study:

• To propose a novel distributed learning consensus scheme for linear and nonlinear MASs.
• To investigate the impact of biased and unbiased randomness on convergence rate and consensus performance.
• To ensure precise consensus tracking under unreliable communication environments.

Main Methods:

• A novel distributed learning consensus scheme is proposed, integrating a distributed control structure, a preliminary correction mechanism, and a separated learning gain/regulation matrix design.
• The scheme's effectiveness is theoretically established through iterationwise asymptotic consensus tracking for linear MAS.
• The methodology is extended to nonlinear MASs with nonidentical initial conditions and diverse gain designs.

Main Results:

• The proposed scheme achieves iterationwise asymptotic consensus tracking for linear MASs, demonstrating its effectiveness under noisy conditions.
• The study analyzes the influence of randomness on convergence rate and consensus performance.
• High-precision tracking performance is achieved for MASs operating under unreliable communication, as verified by simulations.

Conclusions:

• The novel distributed learning consensus scheme effectively ensures precise consensus tracking in MASs with faded neighborhood information.
• The theoretical framework rigorously demonstrates the scheme's capability to handle unreliable communication channels.
• The findings highlight the potential for robust consensus achievement in networked systems facing communication uncertainties.