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A Model Predictive Control Scheme for Consensus in Multi-Agent Systems with Single-Integrator Dynamics and Input Constraints

机译:单积分动态和输入约束多智能体系中的多种子体系统共识模型预测控制方案

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In this paper we address the problem of driving a group of agents towards a consensus point when agents have a discrete-time single-integrator dynamics and the communication graph is undirected and time-varying. We propose a decentralized Model Predictive Control (MPC) scheme that takes into account constraints on the agent inputs and show that it guarantees consensus under mild assumptions. Since the global cost does not decrease monotonically, it cannot be used as a Lyapunov function for proving consensus. Rather, our proof exploits geometric properties of the optimal path followed by individual agents.
机译:在本文中,当代理具有离散时间单积分动态动态和通信图是非传递和时变的时,我们解决了向共识点驱动一组代理的问题。我们提出了一个分散的模型预测控制(MPC)方案,考虑到代理商的限制,并显示其保证在温和假设下的共识。由于全球成本不会单调减少,因此它不能用作Lyapunov功能,以证明共识。相反,我们的证据利用最佳路径的几何属性,然后是个别代理。

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