Tuesday, 14:15 - 14:40 h, Room: H 0112


Francesco Borrelli
Real-time stochastic predictive control applied to building control systems

Coauthors: Matusko Jadranko, Yudong Ma


The presentation will focus on the solution of linear
stochastic model predictive control (SMPC) subject to joint
chance constraints. We present and compare two approaches.
In the the explicit approach a set of unknowns representing allowable violation for each constraint (the risk) is introduced. A tailored interior point method is proposed to explore the special structure of the resulting SMPC problem computing the input sequence and the risk allocation. In the sample-based approach, a large number of stochastic samples is used to transform the SMPC problem into a deterministic one with the original constraints evaluated in every sample. The proposed methods are applied to a building control problem which minimizes energy usage while keeping zone thermal comfort by using uncertain prediction of thermal loads and ambient temperature. Extensive numerical and experimental tests are use to analyze the conservatism and the effectiveness of the proposed approaches.


Talk 3 of the invited session Tue.2.H 0112
"Real-time optimization II" [...]
Cluster 16
"Nonlinear programming" [...]


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