J. Unger, M. Kozek, S. Jakubek:
"Reduced Order Optimization For Model Predictive Control Using Principal Control Moves";
Journal of Process Control, 22 (2012), 1; S. 272 - 279.

Kurzfassung englisch:
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

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