Distributed Model Predictive Control of Large-Scale Systems

  • Aswin N. Venkat
  • James B. Rawlings
  • Stephen J. Wright
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 358)


Completely centralized control of large, networked systems is impractical. Completely decentralized control of such systems, on the other hand, frequently results in unacceptable control performance. In this article, a distributed MPC framework with guaranteed feasibility and nominal stability properties is described. All iterates generated by the proposed distributed MPC algorithm are feasible and the distributed controller, defined by terminating the algorithm at any intermediate iterate, stabilizes the closed-loop system. The above two features allow the practitioner to terminate the distributed MPC algorithm at the end of the sampling interval, even if convergence is not attained. Further, the distributed MPC framework achieves optimal systemwide performance (centralized control) at convergence. Feasibility, stability and optimality properties for the described distributed MPC framework are established. Several examples are presented to demonstrate the efficacy of the proposed approach.


Model Predictive Control Composite Model Distillation Column Manipulate Variable 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-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Aswin N. Venkat
    • 1
  • James B. Rawlings
    • 2
  • Stephen J. Wright
    • 3
  1. 1.Westhollow Technology CenterShell Global Solutions (US) Inc.HoostonUSA
  2. 2.Department of Chemical and Biological EngineeringUniversity of WisconsinMadisonUSA
  3. 3.Computer Sciences Department University of WisconsinMadisonUSA

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