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arXiv 1506.03371math.OCcs.SYeess.SY
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通过鲁棒优化求解达-避概率的上界

Upper bounds for the reach-avoid probability via robust optimization

Automatic Control Laboratory, Department of Information Technology;Electrical Engineering, ETH Z\"urich, Z\"urich 8092, Switzerland

Nikolaos Kariotoglou, Maryam Kamgarpour, Tyler H. Summers, John Lygeros

AI总结:

本文通过可解的凸优化问题构建达-避概率的上界,利用高斯径向基函数的线性组合和结构假设,提出递归半正定规划方法,为大状态-输入空间维度问题提供更优的控制策略。

摘要:

我们考虑离散时间随机系统的有限时间达-避问题。我们的目标是通过可解的凸优化问题构造达-避概率的上界。我们通过限制关注高斯径向基函数的线性组合,并对随机过程的转移核以及达-避问题的目标和安全集施加结构假设来实现这一目标。特别是,我们要求核可以表示为高斯混合密度,其中每个分布的均值是当前状态和输入的仿射函数,目标和安全集可以表示为二次不等式的交集。利用这些结构假设,我们提出了一个递归的半正定规划公式,每一步都提供达-避问题值函数的上界。这些上界提供了一个性能指标,可用于比较任何次优控制策略,并可自身用于构造次优控制策略。通过数值示例表明,即使所得到的界是保守的,相关的控制策略在大状态-输入空间维度(超过20)的问题中也比启发式控制器实现更高的达-避概率。本文的结果远超当前近似方法在所考虑的特定随机系统类别的极限。

英文摘要:

We consider finite horizon reach-avoid problems for discrete time stochastic systems. Our goal is to construct upper bound functions for the reach-avoid probability by means of tractable convex optimization problems. We achieve this by restricting attention to the span of Gaussian radial basis functions and imposing structural assumptions on the transition kernel of the stochastic processes as well as the target and safe sets of the reach-avoid problem. In particular, we require the kernel to be written as a Gaussian mixture density with each mean of the distribution being affine in the current state and input and the target and safe sets to be written as intersections of quadratic inequalities. Taking advantage of these structural assumptions, we formulate a recursion of semidefinite programs where each step provides an upper bound to the value function of the reach- avoid problem. The upper bounds provide a performance metric to which any suboptimal control policy can be compared, and can themselves be used to construct suboptimal control policies. We illustrate via numerical examples that even if the resulting bounds are conservative, the associated control policies achieve higher reach-avoid probabilities than heuristic controllers for problems of large state-input space dimensions (more than 20). The results presented in this paper, far exceed the limits of current approximation methods for reach-avoid problems in the specific class of stochastic systems considered.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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