Distributed Model Predictive Control: Multiple-Controller Cooperation

  • Panagiotis D. Christofides
  • Jinfeng Liu
  • David Muñoz de la Peña
Part of the Advances in Industrial Control book series (AIC)


In Chap. 5, the results of Chap.  4 are extended to distributed model predictive control of large-scale nonlinear systems in which several distinct sets of manipulated inputs are used to regulate the system. Two distributed control architectures designed via Lyapunov-based model predictive control technique are presented. In the first architecture, the distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bidirectional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. The case in which continuous state feedback is available to all the distributed controllers is first considered and then the results are extended to include large-scale nonlinear systems subject to asynchronous and delayed state feedback. The theoretical results are illustrated through a catalytic alkylation of benzene process example. Moreover, an approach to handle communication disruptions and data losses between the distributed controllers is discussed.


Feasibility Problem Prediction Horizon Nominal System Stability Constraint Lyapunov Function Versus 
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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Panagiotis D. Christofides
    • 1
  • Jinfeng Liu
    • 1
  • David Muñoz de la Peña
    • 2
  1. 1.Department of Chemical and Biomolecular EngineeringUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Departamento de Ingeniería de Sistemas y AutomáticaUniversidad de SevillaSevillaSpain

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