Guaranteed cost consensus protocol design for linear multi-agent systems with sampled-data information: An input...

Area of Science:

• Control Theory
• Systems Engineering
• Networked Systems

Background:

• Sampled-data systems introduce complexities in achieving consensus.
• Energy consumption is a critical factor in multi-agent system design.
• Guaranteed cost control aims to minimize performance degradation under uncertainty.

Purpose of the Study:

• To investigate and minimize energy consumption in sampled-data consensus processes.
• To develop a robust method for guaranteed cost consensus in linear multi-agent systems.
• To provide a framework for designing efficient control protocols.

Main Methods:

• Formulating the problem as a guaranteed cost stabilization task.
• Utilizing an input delay approach to create an equivalent system representation.
• Applying linear matrix inequalities (LMIs) for condition derivation.
• Employing time-dependent Lyapunov functional analysis for theoretical rigor.

Main Results:

• A sufficient condition for guaranteed cost consensus was established using LMIs.
• Reduced-order protocol design methodologies were proposed.
• Techniques for optimizing protocol gain and extending sampling intervals were discussed.
• Simulation results validated the effectiveness of the developed theoretical framework.

Conclusions:

• The proposed methods effectively address guaranteed cost consensus for sampled-data linear multi-agent systems.
• The findings contribute to understanding and reducing energy consumption in such systems.
• The developed protocols and analysis provide practical tools for system design and optimization.