Marco Di Pietro

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In this paper we propose an application of distributed model predictive control techniques to the problem of driving a group of autonomous agents towards a consensus point, i.e. a negotiated position in their state space. Agents are assumed to be governed by discretetime singleor double-integrator dynamics and the communication network can be directed and(More)
In this paper we address the problem of driving a group of agents towards a consensus point when agents have a discrete-time integrator dynamics and the communication graph is time-varying. We propose two decentralized Model Predictive Control (MPC) schemes that take into account constraints on the agents’ inputs and show that they guarantee consensus under(More)
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