具有动态事件触发通信的预设时间收敛分布式多目标优化
Prescribed-Time Convergent Distributed Multiobjective Optimization With Dynamic Event-Triggered Communication
AI总结：
本文研究多智能体网络中的分布式约束多目标资源分配问题，通过将其转化为单目标加权$L_p$问题，提出一种结合预设时间控制和动态事件触发机制的新型分布式算法，在预设时间内实现高效通信与求解，并排除了Zeno行为。
摘要：
本文研究多智能体网络中的分布式约束多目标资源分配问题（DCMRAPs），其中智能体在局部和全局约束下面临多个冲突的局部目标。通过将DCMRAPs重新表述为单目标加权$L_p$问题，所提出的方法实现了分布式求解，而无需依赖预定义的加权系数或集中式决策。利用预设时间控制和动态事件触发机制（ETMs），提出了一种通过采样通信在预设时间内运行的新型分布式算法。该算法使用基于广义时间的生成器（TBGs），在优化求解精度和轨迹平滑性方面提供了更大的灵活性，且不受初始条件的限制。与广义TBGs集成的新型动态ETMs通过适应局部误差度量和基于网络的不一致量来提高通信效率，同时在平衡求解精度和通信频率方面提供了增强的灵活性。排除了Zeno行为。通过Lyapunov分析和仿真实验验证，与现有方法相比，该方法展现出更优的控制性能和效率，推动了各种应用中的分布式优化发展。
英文摘要：
This paper addresses distributed constrained multiobjective resource allocation problems (DCMRAPs) in multi-agent networks, where agents face multiple conflicting local objectives under local and global constraints. By reformulating DCMRAPs as single-objective weighted $L_p$ problems, the proposed approach enables distributed solutions without relying on predefined weighting coefficients or centralized decision-making. Leveraging prescribed-time control and dynamic event-triggered mechanisms (ETMs), a novel distributed algorithm is proposed within a prescribed time through sampled communication. Using generalized time-based generators (TBGs), the algorithm provides more flexibility in optimizing solution accuracy and trajectory smoothness without the constraints of initial conditions. Novel dynamic ETMs, integrated with generalized TBGs, improve communication efficiency by adapting to local error metrics and network-based disagreements, while providing enhanced flexibility in balancing solution accuracy and communication frequency. The Zeno behavior is excluded. Validated by Lyapunov analysis and simulation experiments, our method demonstrates superior control performance and efficiency compared to existing methods, advancing distributed optimization across diverse applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
