ALADIN-Based Distributed Model Predictive Control with Dynamic Partitioning: An Application to Solar Parabolic Trough Plants

Abstract

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 min-imum performance losses, the number of variables to be coordinated. Numerical results on a 10-loop plant are presented.