  This article presents a distributed model predictive controller with
time-varying partitioning based on the augmented Lagrangian alternating
direction inexact Newton method (ALADIN). In particular, we address the problem
of controlling the temperature of a heat transfer fluid (HTF) in a set of loops
of solar parabolic collectors by adjusting its flow rate. The control problem
involves a nonlinear prediction model, decoupled inequality constraints, and
coupled affine constraints on the system inputs. The application of ALADIN to
address such a problem is combined with a dynamic clustering-based partitioning
approach that aims at reducing, with minimum performance losses, the number of
variables to be coordinated. Numerical results on a 10-loop plant are
presented.
