Distributed Model Reference Adaptive Optimization of Disturbed Multiagent Systems With Intermittent Communications.

Area of Science:

• Control Systems
• Optimization Theory
• Multiagent Systems

Background:

• Distributed optimization is crucial for multiagent systems but faces challenges from exogenous disturbances and communication constraints.
• Existing methods often assume continuous communication, limiting practical applicability.

Purpose of the Study:

• To propose a novel distributed model reference adaptive control (D-MRAC) scheme for multiagent systems facing exogenous disturbances.
• To enable discrete-time communication between agents, enhancing realism and applicability.
• To improve robustness and transient performance compared to existing methods.

Main Methods:

• Development of a D-MRAC scheme that does not require explicit disturbance observers or internal model units.
• Analysis using Lyapunov stability theory to establish convergence conditions based on communication intervals.
• Validation through numerical simulations to demonstrate effectiveness.

Main Results:

• The proposed D-MRAC scheme effectively solves the distributed optimization problem under exogenous disturbances.
• Robustness and transient performance are enhanced due to the integrated adaptive control approach.
• The method is proven effective provided the communication interval is below a calculated threshold.

Conclusions:

• The novel D-MRAC scheme offers a robust and efficient solution for distributed optimization in multiagent systems with discrete communication.
• This approach overcomes limitations of continuous communication assumptions, paving the way for more practical applications.
• The findings are supported by theoretical analysis and numerical simulations.