Robust consensus tracking control for multiagent systems with initial state shifts, disturbances, and switching...

Area of Science:

• Control Theory
• Networked Systems

Background:

• Multiagent systems (MAS) face challenges in achieving coordinated behavior.
• Consensus tracking is crucial for distributed decision-making and task execution in MAS.
• Existing methods often struggle with dynamic uncertainties and network changes.

Purpose of the Study:

• To develop a robust iterative learning control approach for finite-time consensus tracking in MAS.
• To address uncertainties including initial state shifts, external disturbances, and switching topologies.
• To provide a rigorous mathematical framework for guaranteeing convergence and feasibility.

Main Methods:

• Iterative learning control (ILC) is employed to refine control actions over iterations.
• Distributed algorithms are designed utilizing nearest neighbor information for each agent.
• Linear Matrix Inequalities (LMIs) are formulated to establish necessary and sufficient conditions for convergence.
• Robustness analysis is performed against time- and iteration-varying uncertainties.

Main Results:

• Convergence of consensus tracking objectives is demonstrated through developed matrix norm conditions.
• Feasibility of the control strategy is guaranteed in the spectral norm sense via LMIs.
• The proposed algorithms exhibit robustness against initial state shifts, disturbances, and switching topologies.
• Simulation examples validate the effectiveness and resilience of the consensus tracking control.

Conclusions:

• The iterative learning approach effectively achieves accurate and robust consensus tracking in multiagent systems.
• The developed LMI-based conditions provide a reliable method for designing and verifying such control systems.
• The proposed method offers a significant advancement in handling dynamic uncertainties within networked control systems.