A Distributed Algorithm to Determine Lower and Upper Bounds in Branch and Bound for Hybrid Model Predictive Control

Amir Firooznia (1) , Romain Bourdais (2) , B. de Schutter (1)

1 DCSC - Delft Center for Systems and Control [Delft]
2 IETR - Institut d'Electronique et de Télécommunications de Rennes

In this work, a class of model predictive control problems with mixed real-valued and binary control signals is considered. The optimization problem to be solved is a constrained Mixed Integer Quadratic Programming (MIQP) problem. The main objective is to derive a distributed algorithm for limiting the search space in branch and bound approaches by tightening the lower and upper bounds of objective function. To this aim, a distributed algorithm is proposed for the convex relaxation of the MIQP problem via dual decomposition. The effectiveness of the approach is illustrated with a case study.

Domaines: Automatique / Robotique

Amir Firooznia, Romain Bourdais, B. de Schutter. A Distributed Algorithm to Determine Lower and Upper Bounds in Branch and Bound for Hybrid Model Predictive Control. 54th IEEE Conference on Decision and Control (CDC), Dec 2015, Osaka, Japan. ⟨10.1109/cdc.2015.7402461⟩. ⟨hal-01253140⟩