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Adaptive Model Predictive Building Control: Set-up of a simulation environment, algorithm development and implementation


R. Franz

Semester Thesis, FS14 (10343)

In this report we investigate model-uncertainty and model-mismatch in model predictive building control and propose an adaptive robust control algorithm. The algorithm is compared to an nominal model predictive controller (nominal MPC) and a offset-free MPC approach. In order to compare the algorithms a simulation environment was set-up. The simulation environment is part of a simulation environment implemented for the Heatreserves project. The controller performance is measured in terms of control costs and output constraint violations. In a second step, we define the ideas of model-uncertainty and model-mismatch. In this context we also define a Performance Bound. Then an introduction to MPC is given on the basis of a nominal MPC problem which we are using for our simulations. In addition we propose a way to modify the nominal MPC problem in order to improve controller performance (i.e.: offset-free control) in presence of model-mismatch. Finally, we propose an adaptive robust MPC algorithm based on a Laguerre model set. The controller performs a set-membership identification for adapting its model online. Furthermore, the control algorithm can cope with model-uncertainty by computing robust control inputs guaranteeing constraint satisfaction for the entire model set. We simulate the behavior of the MPC algorithms set forth herein. In order to investigate the effect of model-mismatch/-uncertainty all controllers are applied to a set of 27 plant models and performance is compared. The results of the simulations are presented and discussed. Finally, we summarize our findings and present ideas for future work.


Type of Publication:

(13)Semester/Bachelor Thesis

D. Sturzenegger

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2014:IFA_5001
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