Distributed Robust Optimization for Disturbed Multiagent Systems With Fixed-Time Synchronized Convergence.

Area of Science:

• Control Engineering
• Distributed Systems
• Optimization Theory

Background:

• Second-order multiagent systems (MASs) face challenges with disturbances and local minima in distributed optimization.
• Achieving synchronized convergence in fixed-time is crucial for coordinated MAS behavior.

Purpose of the Study:

• To develop a fixed-time synchronized convergence method for disturbed second-order MASs using the zero-gradient-sum (ZGS) scheme.
• To enhance disturbance rejection, avoid local minima, and ensure privacy preservation in distributed optimization.

Main Methods:

• A fixed-time ZGS distributed optimization method via sliding mode was proposed for second-order MASs.
• A hierarchical robust optimization method with a virtual MAS and a fixed-time synchronized sliding mode tracking controller was introduced.

Main Results:

• The proposed method ensures time-synchronized convergence of agent states and avoids local minima.
• It demonstrates improved disturbance rejection, low conservatism in convergence time bounds, and privacy preservation.
• Numerical simulations validated the effectiveness of the developed methods.

Conclusions:

• The hierarchical robust optimization method effectively achieves fixed-time synchronized convergence for disturbed second-order MASs.
• The approach offers enhanced performance, robustness, and privacy compared to existing protocols.
• This work advances distributed optimization techniques for complex multiagent systems.