Dinesh
Krishnamoorthy, Bjarne Foss and Sigurd Skogestad, A Distributed Algorithm for Scenario-based Model
Predictive Control
using Primal Decomposition
Published in:
IFAC ADCHEM 2018
Abstract: In this paper, we consider
the decomposition of scenario-based model predictive control problem. Scenario
MPC explicitly considers the concept of recourse by representing the evolution
of uncertainty by a discrete scenario tree, which can result in large
optimization problems. Due to the inherent nature of the scenario tree, the
problem can be decomposed into each scenario. The different
subproblems are only coupled via the non-anticipativity constraints which
ensures that the first control input is the same for all the scenarios. This
constraint is relaxed in the dual decomposition approaches, which may lead to
infeasibility of the non-anticipativity constraints
if the master problem does not converge within the required time. In this
paper, we present an alternative approach using primal decomposition
which ensures feasibility of the non-anticipativity
constraints throughout the iterations. The proposed method is demonstrated
using gas-lift optimization as case study.
This
was presented in the Keynote session on Model
Predictive Control and was selected as a finalist (top 3) for IFAC Young Author
Award.