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 discrete-time single- or double-integrator dynamics and the communication network can be directed and time-varying. Our control protocols are called contractive due to a specific constraint imposed on the agents' state path. Consensus is formally proven, also in presence of bounds on the norm of the inputs, by means of a geometrical analysis of the optimal paths.
Contractive distributed MPC for consensus in networks of single- and double-integrators
FERRARI TRECATE, GIANCARLO;
2008-01-01
Abstract
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 discrete-time single- or double-integrator dynamics and the communication network can be directed and time-varying. Our control protocols are called contractive due to a specific constraint imposed on the agents' state path. Consensus is formally proven, also in presence of bounds on the norm of the inputs, by means of a geometrical analysis of the optimal paths.File in questo prodotto:
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