Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction
Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction
复制标题
通过抽象使未知 Lipschitz 连续系统的数据驱动模型无效
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Sze Zheng Yong
中科院分区：
文献类型：
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作者：
Zeyuan Jin;Mohammad Khajenejad;Sze Zheng Yong
本文研究了Lipschitz连续系统的数据驱动模型失效问题，其中不需要给出系统的数学模型，而只需要系统的先验噪声采样数据.我们表明，这种数据驱动的模型失效问题可以使用一个易于处理的可行性检查来解决。我们提出的方法包括两个主要组成部分：（i）数据驱动的抽象部分，它使用噪声采样数据来过度近似未知的Lipschitz连续动态与上下函数，以及（ii）基于优化的模型失效组件，它确定数据驱动的抽象与新观察到的长度T输出轨迹的不兼容性。最后，我们讨论了几种降低算法计算复杂度的方法，并通过一个群体意图识别的仿真例子证明了它们的有效性。
In this paper, we consider the data-driven model invalidation problem for Lipschitz continuous systems, where instead of given mathematical models, only prior noisy sampled data of the systems are available. We show that this data-driven model invalidation problem can be solved using a tractable feasibility check. Our proposed approach consists of two main components: (i) a data-driven abstraction part that uses the noisy sampled data to over-approximate the unknown Lipschitz continuous dynamics with upper and lower functions, and (ii) an optimization-based model invalidation component that determines the incompatibility of the data-driven abstraction with a newly observed length-T output trajectory. Finally, we discuss several methods to reduce the computational complexity of the algorithm and demonstrate their effectiveness with a simulation example of swarm intent identification.