Covariance Steering of Discrete-Time Stochastic Linear Systems Based on Wasserstein Distance Terminal Cost

Covariance Steering of Discrete-Time Stochastic Linear Systems Based on Wasserstein Distance Terminal Cost
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基于Wasserstein距离终端成本的离散时间随机线性系统协方差导向

DOI:
10.1109/lcsys.2020.3047132
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发表时间:
2021
影响因子:
3
通讯作者:
Bakolas, Efstathios
Bakolas, Efstathios
中科院分区:
--
文献类型:
--
作者:
Balci, Isin M.;Bakolas, Efstathios

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我们考虑了一类离散时间线性系统的随机最优控制问题,其目标是控制策略的表征,这些控制策略将引导系统终端状态的概率分布接近期望的高斯分布。在我们的问题表述中,终端状态分布与期望(目标)分布之间的紧密程度是根据与相应的终端成本项相关的瓦瑟斯坦距离的平方来测量的。我们将随机最优控制问题重新定义为一个有限维非线性程序,其性能指标可以表示为两个凸函数的差。性能指标的这种表示允许我们通过所谓的凹凸过程[1]找到原始非线性程序的局部极小值。最后,我们提出了非平凡的数值模拟,通过将其与顺序二次规划方法在计算时间方面进行比较,来证明所提出的技术的有效性。
We consider a class of stochastic optimal control problems for discrete-time linear systems whose objective is the characterization of control policies that will steer the probability distribution of the terminal state of the system close to a desired Gaussian distribution. In our problem formulation, the closeness between the terminal state distribution and the desired (goal) distribution is measured in terms of the squared Wasserstein distance which is associated with a corresponding terminal cost term. We recast the stochastic optimal control problem as a finite-dimensional nonlinear program whose performance index can be expressed as the difference of two convex functions. This representation of the performance index allows us to find local minimizers of the original nonlinear program via the so-called convex-concave procedure [1]. Finally, we present non-trivial numerical simulations to demonstrate the efficacy of the proposed technique by comparing it with sequential quadratic programming methods in terms of computation time.
受积分二次状态约束的随机线性系统的最优协方差控制
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