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Nonnegative Consensus Tracking of Networked Systems With Convergence Rate Optimization
Summary
This study addresses networked systems consensus tracking using distributed static output-feedback control. New methods optimize convergence rates for multi-input multi-output (MIMO) and single-input multi-output (SIMO) positive dynamic systems.
Area of Science:
- Control Systems Engineering
- Networked Systems Theory
- Positive Systems Theory
Background:
- Networked systems consensus tracking is crucial for coordinated agent behavior.
- Designing distributed static output-feedback (SOF) controllers is more complex than state-feedback.
- Agents are modeled as multi-input multi-output (MIMO) positive dynamic systems, potentially with uncertainties.
Purpose of the Study:
- To investigate the nonnegative consensus tracking problem for networked systems using distributed SOF control.
- To develop conditions for consensus in nominal and uncertain networked positive systems.
- To propose optimization methods for convergence rates.
Main Methods:
- Application of positive systems theory to derive consensus conditions.
- Development of semidefinite programming (SDP) approaches for MIMO agent convergence rate optimization.
- Utilization of linear programming (LP) for single-input multi-output (SIMO) agent convergence rate optimization.
Main Results:
- Necessary and sufficient conditions for consensus in networked positive systems were established.
- SDP-based methods were proposed for optimizing MIMO agent consensus convergence.
- LP-based methods were proposed for optimizing SIMO agent consensus convergence.
Conclusions:
- The developed SOF control strategies effectively achieve consensus tracking in networked positive systems.
- The proposed optimization techniques enhance the convergence speed of agent coordination.
- Case studies validated the theoretical results and practical applicability of the methods.
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