Sparse control using sum-of-norms regularized model predictive control
Sparse control using sum-of-norms regularized model predictive control
复制标题
使用范数和正则化模型预测控制的稀疏控制
DOI：
10.1109/cdc.2013.6760797
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
L. Ljung
中科院分区：
文献类型：
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作者：
S. Pakazad;Henrik Ohlsson;L. Ljung
一些控制应用需要使用分段恒定或脉冲型控制信号，变化尽可能少。为了实现这种类型的控制，我们考虑使用正则化模型预测控制（MPC），它允许我们通过使用正则化来强加这种结构。然后可以通过调整所谓的正则化参数来调节控制性能和控制信号特性之间的权衡。然而，由于上述权衡仅间接受到此参数的影响，因此其调优通常不直观且耗时。在本文中，我们提出了正则化MPC的等效重新表述，这使我们能够以更直观和计算效率更高的方式配置所需的权衡。这种重新表述的灵感来自于所谓的多目标优化问题的ε-约束表述，并使我们能够通过显式地分配控制性能的界限来量化权衡。
Some control applications require the use of piecewise constant or impulse-type control signals, with as few changes as possible. So as to achieve this type of control, we consider the use of regularized model predictive control (MPC), which allows us to impose this structure through the use of regularization. It is then possible to regulate the trade-off between control performance and control signal characteristics by tuning the so-called regularization parameter. However, since the mentioned trade-off is only indirectly affected by this parameter, its tuning is often unintuitive and time-consuming. In this paper, we propose an equivalent reformulation of the regularized MPC, which enables us to configure the desired trade-off in a more intuitive and computationally efficient manner. This reformulation is inspired by the so-called ε-constraint formulation of multi-objective optimization problems and enables us to quantify the trade-off, by explicitly assigning bounds over the control performance.