共享通信约束下的多智能体事件触发LQG控制
Multi-Agent Event-Triggered LQG Control under Shared Communication Constraints
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中文总结 AI 辅助
本文针对共享通信容量受限的多智能体系统，提出基于MILP的滚动时域调度器及拍卖启发式调度器，实现事件触发LQG控制，在性能与通信成本间取得最佳权衡。
中文摘要 AI 辅助
本文研究多智能体系统在共享通信网络且每步容量有限条件下的事件触发线性二次高斯（LQG）控制问题。尽管各智能体的动态是解耦的，但通信决策通过共享网络约束相互耦合，从而形成一个受约束的多智能体调度问题。我们证明，最优控制律保持确定性等价性，并通过独立的有限时域Riccati递推在各智能体间解耦，而传输调度则保持全局耦合。基于这一结构，我们开发了一个集中式滚动时域调度框架，并利用估计误差协方差的闭式表征，将由此产生的问题重构为混合整数线性规划（MILP）。为提高可扩展性，我们推导了一个基于窗口的跳跃剪枝条件，该条件在求解MILP之前安全地将连续的传输决策固定为零，并提出了一种基于一步传输收益评分的拍卖启发式调度器。数值结果表明，所提出的模型预测控制（MPC）调度器在控制性能与通信成本之间取得了最佳权衡，而基于拍卖的调度器在显著降低计算复杂度的同时，性能接近MPC。
英文摘要
This letter studies event-triggered linear-quadratic-Gaussian (LQG) control for multi-agent systems sharing a communication network with limited per-step capacity. Although the agent dynamics are decoupled, the communication decisions are coupled through the shared network constraint, leading to a constrained multi-agent scheduling problem. We show that the optimal control law remains certainty-equivalent and decouples across agents through independent finite-horizon Riccati recursions, whereas the transmission schedule remains globally coupled. Based on this structure, we develop a centralized receding-horizon scheduling framework and reformulate the resulting problem as a mixed-integer linear program (MILP) using a closed-form characterization of the estimation-error covariance. To improve scalability, we derive a window-based skip-pruning condition that safely fixes consecutive transmission decisions to zero before solving the MILP, and we propose an auction-inspired scheduler based on one-step transmission-benefit scores. Numerical results show that the proposed model predictive control (MPC) scheduler achieves the best trade-off between control performance and communication cost, while the auction-based scheduler attains performance close to MPC with substantially lower computational complexity.
发表机构
- University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
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