Active-passive dynamic consensus filters consist of agents subject to local observations of a process (i.e., active agents) and agents without any observations (i.e., passive agents). The key feature of these filters is that they enable the states of all agents to converge to the average of the observations only sensed by the active agents. Two sweeping generalizations can be made about existing active-passive dynamic consensus filters: 1) they utilize integral action-based distributed control algorithms such that each agent is required to continuously exchange both its current state and integral state information with its neighbors; and 2) they assume that the roles of active and passive agents are fixed ; hence, these roles do not change with respect to time. The contribution of this paper is to introduce and analyze a new class of active-passive dynamic consensus filters using results from graph theory and systems science. Specifically, the proposed filters only require agents to exchange their current state information with neighbors in a simple and isotropic manner to reduce the overall information exchange cost of the network. In addition, we allow the roles of active and passive agents to be time-varying for making these filters suitable for a wide range of multiagent systems applications. We show that the proposed active-passive dynamic consensus filters enable the states of all agents to converge to an user-adjustable neighborhood of the average of the observations sensed by a time-varying set of active agents. We also generalize our results using event-triggered control theory such that agents schedule information exchange dependent on errors exceeding user-defined thresholds ( not continuously). This generalization allows agents to further reduce the overall cost of interagent information exchange and to determine when to broadcast their information to their neighbors thus eliminating the need to synchronize their states. Four illustrative numerical examples and one experimental study are also presented to demonstrate our theoretical findings.

中文翻译：

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具有减少信息交换和时变代理角色的主动-被动动态共识过滤器

主动-被动动态共识过滤器由受过程局部观察（例如， 活性 代理商）和没有任何观察力的代理商（即 被动代理商）。这些过滤器的关键特征是它们使所有代理的状态收敛到观测值的平均值只要由活性剂感知。关于现有的主动-被动动态共识过滤器，可以有两种概括的概括：1）它们利用基于动作的积分式分布式控制算法，因此需要每个代理连续交换其两个当前状态 和 整体状态与邻居的信息；2）他们假设主动和被动代理的角色是固定 ; 因此，这些角色确实不随时间变化。本文的贡献是利用图论和系统科学的结果介绍和分析了一类新的主动-被动动态共识滤波器。具体来说，建议的过滤器只要 要求代理商交换他们的 当前状态以简单且各向同性的方式与邻居共享信息，以减少网络的总体信息交换成本。此外，我们允许主动和被动代理的角色随时间变化使这些过滤器适用于多种多代理系统应用。我们表明，提出的主动-被动动态共识过滤器可使所有代理的状态收敛到一个用户可调随时间变化的一组活动代理感知到的平均观测值的邻域。我们还使用事件触发的控制理论来概括我们的结果，以便代理根据超过用户定义的阈值的错误来调度信息交换（ 不连续）。这种概括使代理可以进一步降低代理间信息交换的总体成本，并确定何时向其邻居广播其信息，从而消除了同步其状态的需要。还提供了四个说明性的数值示例和一个实验研究来证明我们的理论发现。