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J. Unger, M. Kozek, S. Jakubek:
"Reduced Order Optimization For Model Predictive Control Using Principal Control Moves";
Poster: NRW Young Scientist Award 2012, Düsseldorf (invited); 2012-11-19.

In order to reduce the computational complexity of model predictive control (MPC) a proper input signal parametrization is proposed in this paper which significantly reduces the number of decision variables. This parametrization can be based on either measured data from closed-loop operation or simulation data. The snapshots of representative time domain data for all manipulated variables are projected on an orthonormal basis by a Karhunen-Loeve transformation. These significant features (termed principal control moves, PCM) can be reduced utilizing an analytic criterion for performance degradation. Furthermore, a stability analysis of the proposed method is given. Considerations on the identification of the PCM are made and another criterion is given for a sufficient selection of PCM. It is shown by an example of an industrial drying process that a strong reduction in the order of the optimization is possible while retaining a high performance level.

Model predictive control, optimization order reduction, principal control moves, Karhunen-Loeve Transformation