@incollection{eprints731,
       publisher = {Springer-Verlag},
          author = {Alberto Bemporad and Davide Barcelli},
       booktitle = {Networked control systems},
          editor = {Alberto Bemporad and W.P.M.H. Heemels and Mikael Johansson},
          volume = {406},
           pages = {149--178},
          series = {Lecture Notes in Control and Information Sciences},
           title = {Decentralized model predictive control },
            year = {2010},
        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.
},
             url = {http://eprints.imtlucca.it/731/}
}