Distributed MPC Via Dual Decomposition and Alternating Direction Method of Multipliers
2013 (English)In: Distributed Model Predictive Control Made Easy / [ed] Rudy R. Negenborn and Jose M. Maestre, Springer, 2013Chapter in book (Refereed)
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.
Place, publisher, year, edition, pages
, Intelligent Systems, Control and Automation: Science and Engineering, 69
Model Predictive Control, Distributed Optimization, Dual Decomposition, Stopping Criteria
IdentifiersURN: urn:nbn:se:kth:diva-111427ISBN: 978-94-007-7005-8OAI: oai:DiVA.org:kth-111427DiVA: diva2:586231
QC 201311072013-01-112013-01-112016-04-19Bibliographically approved