论文标题
具有度量时间逻辑规格的多代理系统的分布式私人控制合成的分布式私有控制合成
Distributed Differentially Private Control Synthesis for Multi-Agent Systems with Metric Temporal Logic Specifications
论文作者
论文摘要
在本文中，我们提出了具有指标时间逻辑（MTL）规格的多代理系统（MAS）的分布式私人退缩的视野控制（RHC）方法。在本文中考虑的MAS中，每个代理都使用差异隐私机制将其敏感信息私有化。换句话说，每个代理都会在其输出中添加隐私噪声（例如高斯噪声），以保持其隐私并与相邻代理传达其嘈杂的输出。我们为MAS定义了两种类型的MTL规格：代理级规格和系统级规格。代理应以最小概率来满足系统级MTL规格，而每个代理必须同时满足自己的代理级MTL规格。在拟议的分布式RHC方法中，每个代理都与其相邻代理进行通信以获取其嘈杂的输出并计算系统级轨迹的估计。然后，每个代理合成了自己的控制输入，以使系统级规范对最小概率满足，而代理级别的规格也得到满足。在提出的RHC的优化公式中，我们直接合并了Kalman滤波器方程来计算系统级轨迹的估计值，并且我们使用混合智能线性编程（MILP）将MTL规格编码为优化约束。最后，我们在案例研究中实施了拟议的分布式RHC方法。
In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications. In the MAS considered in this paper, each agent privatizes its sensitive information from other agents using a differential privacy mechanism. In other words, each agent adds privacy noise (e.g., Gaussian noise) to its output to maintain its privacy and communicates its noisy output with its neighboring agents. We define two types of MTL specifications for the MAS: agent-level specifications and system-level specifications. Agents should collaborate to satisfy the system-level MTL specifications with a minimum probability while each agent must satisfy its own agent-level MTL specifications at the same time. In the proposed distributed RHC approach, each agent communicates with its neighboring agents to acquire their noisy outputs and calculates an estimate of the system-level trajectory. Then each agent synthesizes its own control inputs such that the system-level specifications are satisfied with a minimum probability while the agent-level specifications are also satisfied. In the proposed optimization formulation of RHC, we directly incorporate Kalman filter equations to calculate the estimates of the system-level trajectory, and we use mixed-integer linear programming (MILP) to encode the MTL specifications as optimization constraints. Finally, we implement the proposed distributed RHC approach in a case study.
