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Regionless Explicit Model Predictive Control
Date: 2016/9/23             Browse: 257

Seminar topic: Regionless Explicit Model Predictive Control


Speaker: Juraj Oravec

Time: Sept. 23, 9:30 a.m. – 10:30 a.m.

Venue: Room 104, H2 Building



We show that explicit MPC solutions admit a closed-form solution which does not require the storage of critical regions. Therefore significant amount of memory can be saved. In fact, not even the construction of such regions is required. Instead, all possible optimal active sets are first extensively enumerated. Then, for each optimal, only the analytical expressions of primal and dual variables are stored. Optimality of a particular if checked by verifying primal and dual feasibility conditions, which are unique for all candidate sets. We show that the required memory storage can be further reduced by only storing the factors for the dual variables. The advantage of such an approach is that it allows to construct explicit MPC solutions even for complex systems. The feasibility of the approach is demonstrated on a laboratory distillation column. We show that the control algorithm requires only modest computational resources on-line. The performance of the proposed regionless explicit MPC strategy is validated using experimentally collected data.


Juraj Oravec received his master in Automation and Information Engineering from the Slovak University of Technology in Bratislava in 2010, and a PhD from the same institute in 2014. Since 2014 he is a postdoctoral researcher working under the supervision of Prof. Michal Kvasnica. His research is on model predictive control with a particular focus on uncertain control systems.

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