Mengna Quan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mengna Quan has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bin Lan
Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China
1 shared publication - Shanlin Sun
Affiliation School of Aeronautics and Astronautics, Guilin University of Aerospace Technology, Guilin, China
1 shared publication - Shike Long
Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China
1 shared publication - Yongjun Wang
Guangxi Key Laboratory of Low-Altitude Unmanned Autonomous System, Guilin University of Aerospace Technology, Guilin 541004, China
1 shared publication
Latest Publications
- Open Access
A Distributed Reinforcement Learning Method for Output Consensus of Heterogeneous Multi-Agent Systems with Event-Triggered Mechanisms
This paper investigates the output consensus problem in heterogeneous multi-agent systems. To address the challenges of traditional analytical methods in handling unknown dynamics and disturbances, a control framework is proposed that combines known model structures with a data-driven adaptive mechanism. The framework uses a distributed internal model to compensate for system heterogeneity and incorporates an event-triggered mechanism to reduce communication burden. To improve transient tracking performance, a reinforcement learning strategy based on centralized training and decentralized execution is introduced to adaptively optimize local feedback gains. Simulation results show that the proposed method effectively bounds closed-loop signals, achieves relatively fast convergence, and demonstrates some robustness and communication efficiency under process noise.
Open AccessA Distributed Reinforcement Learning Method for Output Consensus of Heterogeneous Multi-Agent Systems with Event-Triggered Mechanisms
This paper investigates the output consensus problem in heterogeneous multi-agent systems. To address the challenges of traditional analytical methods in handling unknown dynamics and disturbances, a control framework is proposed that combines known model structures with a data-driven adaptive mechanism. The framework uses a distributed internal model to compensate for system heterogeneity and incorporates an event-triggered mechanism to reduce communication burden. To improve transient tracking performance, a reinforcement learning strategy based on centralized training and decentralized execution is introduced to adaptively optimize local feedback gains. Simulation results show that the proposed method effectively bounds closed-loop signals, achieves relatively fast convergence, and demonstrates some robustness and communication efficiency under process noise.