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Decentralized Model Predictive Control

  • Alberto Bemporad
  • Davide Barcelli
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 406)

Abstract

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.

Keywords

Packet Loss Model Predictive Control Packet Dropout Recede Horizon Control Model Predictive Control Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer London 2010

Authors and Affiliations

  • Alberto Bemporad
    • 1
  • Davide Barcelli
    • 2
  1. 1.Department of Mechanical and Structural EngineeringUniversity of TrentoItaly
  2. 2.Department of Information EngineeringUniversity of SienaItaly

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