TY  - CHAP
EP  - 178
T2  - Networked control systems
ID  - eprints731
UR  - http://dx.doi.org/10.1007/978-0-85729-033-5_5
TI  - Decentralized model predictive control 
AV  - none
M1  - 406
N2  - Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way. Compared to a centralized MPC setup, where a global optimal control problem must be solved on-line with respect to all actuator commands given the entire set of states, in DMPC the control problem is divided into a set of local MPCs of smaller size, that cooperate by communicating each other a certain information set, such as local state measurements, local decisions, optimal local predictions. Each controller is based on a partial (local) model of the overall dynamics, possibly neglecting existing dynamical interactions. The global performance objective is suitably mapped into a local objective for each of the local MPC problems.
This chapter surveys some of the main contributions to DMPC, with an emphasis on a method developed by the authors, by illustrating the ideas on motivating examples. Some novel ideas to address the problem of hierarchical MPC design are also included in the chapter.

SN  - 978-0-85729-032-8
ED  - Bemporad, Alberto
ED  - Heemels, W.P.M.H.
ED  - Johansson, Mikael
Y1  - 2010///
SP  - 149
T3  - Lecture Notes in Control and Information Sciences
A1  - Bemporad, Alberto
A1  - Barcelli, Davide
PB  - Springer-Verlag
ER  -