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Noncooperative Model Predictive Game for Uncertain Multiagent Systems: A Dual-Mode Control Strategy.
IEEE Transactions on Cybernetics
|June 19, 2026
Summary
This study introduces a dual-mode distributed model predictive control (DMPC) for multiagent systems (MASs) in noncooperative games. The novel approach ensures consensus and anti-interference, enabling agents to reach an ε-Nash equilibrium efficiently.
Area of Science:
- Control Engineering
- Game Theory
- Distributed Systems
Background:
- Multiagent systems (MASs) face challenges in coordinating behavior due to inter-agent influence and external disturbances.
- Noncooperative game theory provides a framework for analyzing strategic interactions among autonomous agents.
- Distributed Model Predictive Control (DMPC) offers a decentralized approach to control MASs, but challenges remain in balancing performance, computation, and robustness.
Purpose of the Study:
- To develop a novel dual-mode distributed model predictive control (DMPC) strategy for multiagent systems (MASs) operating within noncooperative game contexts.
- To design an objective function that accounts for model performance, control cost, consensus, and anti-interference capabilities.
- To establish sufficient conditions for stability and an algorithm ensuring convergence to an ε-Nash equilibrium (ε-NE).
Main Methods:
- A novel objective function incorporating consensus and anti-interference terms is proposed.
- A dual-model control strategy is introduced to balance computational load and performance.
- Techniques including the quadratic boundedness lemma, Rayleigh-Ritz theorem, and slack matrix method are employed to solve online and offline problems.
- An iterative DMPC-based algorithm is designed for ε-NE convergence.
Main Results:
- The proposed DMPC framework effectively handles inter-agent couplings and neighborly influences.
- Solvability of both online and offline control problems is established.
- Sufficient conditions for system stability are derived.
- The iterative algorithm guarantees convergence of MASs to the ε-NE.
Conclusions:
- The developed dual-mode DMPC is effective for noncooperative MASs, achieving consensus and robustness.
- The strategy provides a practical solution for MAS control, balancing computational burden and performance.
- Simulations on a spacecraft system validate the distributed convergence to the ε-NE.
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