Relaxed Logarithmic Barrier Function Based Model Predictive Control of Linear Systems
Relaxed Logarithmic Barrier Function Based Model Predictive Control of Linear Systems
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DOI：
10.1109/tac.2016.2582040
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发表时间：
2015-03
影响因子：
6.8
通讯作者：
Christian Feller;C. Ebenbauer
中科院分区：
文献类型：
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作者：
Christian Feller;C. Ebenbauer
在本文中，我们研究了使用放松对数障碍函数的线性模型预测控制的背景下。我们提出的结果，允许保证相应的闭环系统的渐近稳定性，并讨论进一步的性能和约束满足依赖于底层的放松。所提出的稳定MPC计划不一定是基于一个明确的终端集或状态约束，并允许表征稳定控制输入序列作为一个全局定义的，连续可微的，强凸函数的最小值。最后通过数值算例说明了结果。
In this paper, we investigate the use of relaxed logarithmic barrier functions in the context of linear model predictive control. We present results that allow to guarantee asymptotic stability of the corresponding closed-loop system, and discuss further properties like performance and constraint satisfaction in dependence of the underlying relaxation. The proposed stabilizing MPC schemes are not necessarily based on an explicit terminal set or state constraint and allow to characterize the stabilizing control input sequence as the minimizer of a globally defined, continuously differentiable, and strongly convex function. The results are illustrated by means of a numerical example.