Distributed Optimization for Disturbed Second-Order Multiagent Systems Based on Active Antidisturbance Control.

Area of Science:

• Control Systems Engineering
• Optimization Theory
• Robotics

Background:

• Distributed optimization is crucial for coordinating multiagent systems.
• Mismatched and matched disturbances pose significant challenges in these systems.
• Existing methods often struggle with complex disturbance scenarios.

Purpose of the Study:

• To develop a distributed active antidisturbance control framework for second-order multiagent systems.
• To address challenges posed by both mismatched and matched disturbances in distributed optimization.
• To achieve consensus to the global cost function minimizer despite external perturbations.

Main Methods:

• A two-stage framework involving disturbance estimation/compensation and distributed composite control.
• Utilizing disturbance estimators for each agent to identify matched/mismatched disturbances.
• Designing distributed controllers using disturbance estimates, neighbor information, and local cost function gradients.

Main Results:

• Agents' outputs asymptotically reach consensus to the global cost function minimizer.
• The closed-loop system convergence is rigorously proven using Lyapunov functions and input-to-state stability.
• Simulation results validate the effectiveness of the proposed antidisturbance control scheme.

Conclusions:

• The proposed distributed active antidisturbance control framework effectively solves optimization problems in second-order multiagent systems.
• The method demonstrates robustness against both matched and mismatched disturbances.
• The approach ensures reliable consensus achievement, validated by theoretical proofs and simulations.