Studies on resilient control through multiagent consensus networks subject to disturbances

Area of Science:

• Control Systems Engineering
• Networked Systems Theory
• Industrial Automation

Background:

• Resiliency is a critical objective in complex industrial applications.
• Designing resilient control systems for networked multiagent consensus systems remains an open challenge.
• Industrial systems often face disturbances and noise, impacting performance and stability.

Purpose of the Study:

• To propose resilient control design guidelines for industrial systems modeled as networked multiagent consensus systems.
• To analyze the input-to-output stability of multiagent consensus networks under various disturbances.
• To establish necessary and sufficient conditions for disturbed multiagent consensus networks using input-to-state stability theory.

Main Methods:

• Analysis of multiagent consensus networks from an input-to-output stability perspective.
• Application of nonsingular linear transformation and input-to-state stability theory.
• Utilizing linear matrix inequality (LMI) techniques for optimal disturbance rejection index determination.

Main Results:

• Established necessary and sufficient conditions for disturbed multiagent consensus networks.
• Analyzed disturbance rejection performance across three distinct cases based on disturbance and state disagreement spaces.
• Demonstrated the effectiveness of LMI for optimizing disturbance rejection.

Conclusions:

• The proposed guidelines offer a robust framework for designing resilient control systems in industrial settings.
• The study provides a theoretical foundation for enhancing disturbance rejection in networked multiagent systems.
• Numerical examples validate the consensus results for diverse network topologies and disturbance types.