Quasi-Consensus Control for Stochastic Multiagent Systems: When Energy Harvesting Constraints Meet Multimodal FDI...

Area of Science:

• Control Theory
• Systems Engineering
• Networked Systems

Background:

• Investigates quasi-consensus control for stochastic nonlinear time-varying multiagent systems (MASs).
• Addresses challenges including energy harvesting and false data-injection (FDI) attacks.
• Generalizes existing results to a probabilistic quasi-consensus objective.

Purpose of the Study:

• To design a distributed control protocol for achieving probabilistic quasi-consensus in MASs.
• To develop a more general multimodal FDI model for enhanced security analysis.
• To provide sufficient conditions for guaranteeing the probabilistic quasi-consensus property.

Main Methods:

• Utilizes probabilistic-constrained analysis techniques.
• Employs recursive linear matrix inequalities (RLMIs) for analysis.
• Designs an optimal probabilistic-constrained algorithm via convex optimization.

Main Results:

• Sufficient conditions are derived to guarantee the probabilistic quasi-consensus property.
• A controller gain derivation method is presented using convex optimization.
• The framework's validity is demonstrated through two illustrative examples.

Conclusions:

• The proposed framework effectively addresses the quasi-consensus control problem for complex MASs.
• The integration of energy harvesting and FDI attack considerations enhances practical applicability.
• The probabilistic approach offers a robust method for ensuring system performance under uncertainty.