Krishnamoorthy,
Dinesh., Suwartadi, Eka., Foss, Bjarne., Skogestad, Sigurd.
and Jäschke, Johannes., Improving Scenario Decomposition for Multistage MPC using a
Sensitivity-based Path-following Algorithm.
Published
in IEEE Control System Letters, Vol 2(4), p.581-586.
Abstract:
This
paper proposes a computationally efficient algorithm for robust multistage
model predictive control (MPC). In multistage scenario MPC, the evolution of
uncertainty in the prediction horizon is represented
via a scenario tree. The resulting large-scale optimization problem can be decomposed into several smaller subproblems
where, for example, each subproblem solves a single scenario.
Since the different scenarios differ only in the uncertain parameters, the
distributed scenario MPC problem can be cast as a
parametric nonlinear programming (NLP) problem. By using the NLP sensitivity,
we do not need to solve all the subproblems as full
NLPs. Instead they can be solved exploiting the
parametric nature by a path-following predictor corrector algorithm that
approximates the NLP. This results in a computationally efficient multistage
scenario MPC framework. Simulation results show that the sensitivity-based
distributed multistage MPC provides a very good approximation of the fully centralized
scenario MPC.
This paper was also accepted
for oral presentation at the 57th IEEE conference on Decision and
Control (CDC), Miami Beach FL.