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On the Use of Suboptimal Solvers for Efficient Cooperative Distributed Linear MPC

  • G. PannocchiaEmail author
  • S. J. Wright
  • J. B. Rawlings
Chapter
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 69)

Abstract

We address the problem of efficient implementations of distributed Model Predictive Control (MPC) systems for large-scale plants. We explore two possibilities of using suboptimal solvers for the quadratic program associated with the local MPC problems. The first is based on an active set method with early termination. The second is based on Partial Enumeration (PE), an approach that allows one to compute the (sub)optimal solution by using a solution table which stores the information of only a few most recently optimal active sets. The use of quick suboptimal solvers, especially PE, is shown to be beneficial because more cooperative iterations can be performed in the allowed given decision time. By using the available computation time for cooperative iterations rather than local iterations, we can improve the overall optimality of the strategy. We also discuss how input constraints that involve different units (for example, on the summation of common utility consumption) can be handled appropriately. Our main ideas are illustrated with a simulated example comprising three units and a coupled input constraint.

Keywords

Quadratic Program Model Predictive Control Decision Time Couple Constraint Model Predictive Controller 
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 Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Civil and Industrial EngineeringUniversity of PisaPisaItaly
  2. 2.Computer Sciences DepartmentUniversity of WisconsinMadisonUSA
  3. 3.Chemical and Biological Engineering DepartmentUniversity of MadisonMadisonUSA

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