Distributed MPC Via Dual Decomposition and Alternative Direction Method of Multipliers

  • F. FarokhiEmail author
  • I. Shames
  • K. H. Johansson
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 69)


A conventional way to handle model predictive control (MPC) problems distributedly is to solve them via dual decomposition and gradient ascent. However, at each time-step, it might not be feasible to wait for the dual algorithm to converge. As a result, the algorithm might be needed to be terminated prematurely. One is then interested to see if the solution at the point of termination is close to the optimal solution and when one should terminate the algorithm if a certain distance to optimality is to be guaranteed. In this chapter, we look at this problem for distributed systems under general dynamical and performance couplings, then, we make a statement on validity of similar results where the problem is solved using alternative direction method of multipliers.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Electrical Engineering, ACCESS Linnaeus CentreKTH Royal Institute of TechnologyStockholmSweden
  2. 2.Department of Electrical and Electronic EngineeringUniversity of MelbourneMelbourneAustralia

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