The Distributed Predictive Control (DPC) algorithm presented in this chapter has been designed for control of an overall system made by linear discretetime dynamically interconnected subsystems. It consists of a non-cooperative, noniterative algorithm where a neighbor-to-neighbor transmission protocol is needed. The DPC algorithm enjoys the following properties: (i) state and input constraints can be considered; (ii) convergence is guaranteed; (iii) it is not necessary for each subsystem to know the dynamical models of the other subsystems; (iv) the transmission of information is limited.

Distributed MPC: A Noncooperative Approach Based on Robustness Concepts

BETTI, GIULIO;FARINA, MARCELLO;SCATTOLINI, RICCARDO
2014-01-01

Abstract

The Distributed Predictive Control (DPC) algorithm presented in this chapter has been designed for control of an overall system made by linear discretetime dynamically interconnected subsystems. It consists of a non-cooperative, noniterative algorithm where a neighbor-to-neighbor transmission protocol is needed. The DPC algorithm enjoys the following properties: (i) state and input constraints can be considered; (ii) convergence is guaranteed; (iii) it is not necessary for each subsystem to know the dynamical models of the other subsystems; (iv) the transmission of information is limited.
2014
Distributed Model Predictive Control Made Easy
9789400770058
9789400770065
AUT
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/960377
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