Accelerating model predictive control by online constraint removal
Accelerating model predictive control by online constraint removal
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DOI：
10.1109/cdc.2013.6760798
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
M. Jost;M. Mönnigmann
中科院分区：
文献类型：
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作者：
M. Jost;M. Mönnigmann
提出了一种利用显式解的部分信息来加速在线线性模型预测控制(MPC)计算的方法。我们强调了该方法的两个特点：(1)它不需要先计算显式解，并且它的计算量只在问题的约束数目中以多项式增长。因此，所提出的方法可以应用于对于当今显式MPC方法来说太大的问题。(2)该方法不是基于优化算法的特定类型或实现，因此可以很容易地与各种现有的预测控制实现相结合。据作者所知，所提出的方法是利用对在线MPC中显性MPC法律结构的洞察的少数尝试之一。
We propose a method for the acceleration of the online linear model predictive control (MPC) calculations with partial information on the explicit solution. We highlight two properties of the proposed approach: (i) It does not require to calculate the explicit solution first, and its computational effort grows only polynomially in the number of the constraints of the problem. The proposed approach can therefore be applied to problems that are too large for today's explicit MPC methods. (ii) The method is not based on a specific type or implementation of the optimization algorithm and can therefore easily be combined with a variety of existing MPC implementations. The proposed approach is, to the knowledge of the authors, one of yet a few attempts to use the insight into the structure of the explicit MPC law in online MPC.