论文标题
带有添加剂和参数不确定性的约束线性系统的柔性鲁棒控制的柔性稳健控制的管子多阶段MPC
Tube-enhanced Multi-stage MPC for Flexible Robust Control of Constrained Linear Systems with Additive and Parametric Uncertainties
论文作者
论文摘要
最佳和复杂性之间的权衡一直是强大模型预测控制（MPC）领域中最重要的挑战之一。为了应对挑战，我们通过协同基于多阶段和基于管的MPC方法来提出一种灵活的强大MPC方案。关键思想是利用多阶段MPC的非保守主义和基于管的MPC的简单性。提出的方案为用户提供了两个选项，可以根据应用程序确定权衡：可靠的视野和不确定性的分类。除了强大的地平线外，借助管的帮助，可以避免在多阶段MPC中采用的场景-tree的分支。通过处理\ emph {small}的不确定性，可以通过不变的管道计算出可以脱机计算的不确定性来减少问题大小相对于不确定性数量的生长。这导致问题大小的线性生长超出了稳健的视野，并且问题大小没有针对少量不确定性的生长。与现有强大的MPC方法相比，所提出的方法有助于实现最佳和复杂性之间的理想权衡。我们表明，所提出的方法在渐近稳定。对于CSTR示例证明了它的优势。
The trade-off between optimality and complexity has been one of the most important challenges in the field of robust Model Predictive Control (MPC). To address the challenge, we propose a flexible robust MPC scheme by synergizing the multi-stage and tube-based MPC approaches. The key idea is to exploit the non-conservatism of the multi-stage MPC and the simplicity of the tube-based MPC. The proposed scheme provides two options for the user to determine the trade-off depending on the application: the choice of the robust horizon and the classification of the uncertainties. Beyond the robust horizon, the branching of the scenario-tree employed in multi-stage MPC is avoided with the help of tubes. The growth of the problem size with respect to the number of uncertainties is reduced by handling \emph{small} uncertainties via an invariant tube that can be computed offline. This results in linear growth of the problem size beyond the robust horizon and no growth of the problem size concerning small magnitude uncertainties. The proposed approach helps to achieve a desired trade-off between optimality and complexity compared to existing robust MPC approaches. We show that the proposed approach is robustly asymptotically stable. Its advantages are demonstrated for a CSTR example.
