This paper considers the cooperation control problem for a team of dynamically decoupled agents with resource constraints. A codesign of self-triggered mechanism and distributed model predictive control (DMPC) is proposed to achieve the cooperative objectives while efficiently exploiting communication network. The proposed self-triggered DMPC (ST-DMPC) possesses three important features. First, the communication cost is explicitly incorporated in the cost function. In this way, the triggering instant and control inputs are simultaneously optimized, and a desired tradeoff between control performance and communication cost is achieved. Second, at triggering instants, the first element of the optimal control input sequence along with the current state instead of the whole trajectory is broadcast to neighbors for cooperation, which further reduces communication load. Third, sufficient conditions on design parameters related to predictive states of neighbor agents are constructed to ensure stability of the overall system. The application of the proposed ST-DMPC to four robot manipulators validates the effectiveness of this method.

中文翻译：

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协作多智能体系统的自触发 DMPC 设计

本文考虑了具有资源约束的动态解耦代理团队的合作控制问题。提出了一种自触发机制和分布式模型预测控制（DMPC）的协同设计，以在有效利用通信网络的同时实现合作目标。所提出的自触发 DMPC (ST-DMPC) 具有三个重要特征。首先，通信成本明确地包含在成本函数中。这样，触发瞬间和控制输入被同时优化，并且在控制性能和通信成本之间实现了期望的折衷。其次，在触发时刻，最优控制输入序列的第一个元素以及当前状态而不是整个轨迹被广播给邻居进行合作，这进一步减少了通信负载。第三，构建与邻居代理的预测状态相关的设计参数的充分条件，以确保整个系统的稳定性。将所提出的 ST-DMPC 应用于四个机器人操纵器验证了该方法的有效性。