Reduced order optimization for model predictive control using principal control moves

详细信息    查看全文

• 作者：Johannes Unger ^{e0526365@student.tuwien.ac.at} ; Martin Kozek ^{kozek@impa.tuwien.ac.at} ; Stefan Jakubek ; ^{jakubek@impa.tuwien.ac.at}
• 关键词：Model predictive control ; Optimization order reduction ; Principal control moves ; Karhunen&ndash ; Loeve transformation
• 刊名：Journal of Process Control
• 出版年：2012
• 出版时间：January 2012
• 年：2012
• 卷：22
• 期：1
• 页码：272-279
• 全文大小：776 K

文摘

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.