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February 13, 2026IEEE Transactions on Cybernetics

Event-Triggered Safety-Stability Framework for Learning-Based Control of Multiagent System With Uncertain Dynamics

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Authors

ZJZhanxiao JiaNorthwestern Polytechnical UniversityTZTao ZhangNorthwestern Polytechnical UniversityJSJinya Su

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Implication

Policy optimization enhances safety and efficiency in multiagent systems with uncertain dynamics, suggesting improved deployment in critical areas.

Key Points

  • The central aim is to develop a framework that ensures safety and enhances performance in multiagent systems during learning and deployment phases.
  • Developed a safety-stability layer integrating RCBFs and ISS-CLFs.
  • Focused on goal-reaching objectives during training to improve exploration while ensuring safety.
  • Implemented a real-time safety-stability layer to filter policy outputs during deployment.
  • Designed an event-triggered mechanism for resource-efficient action compensation calculation.
  • Demonstrated adherence to strict safety constraints during operation.
  • Significantly improved task execution efficiency in multiagent systems.
  • Validated effectiveness through simulations in dynamic multiunicycle environments.

Cite This Study

Jia et al. (2026) studied this question.

synapsesocial.com/papers/698ebeb185a1ff6a93016099https://doi.org/10.1109/tcyb.2026.3658873
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