论文标题
通过抽象的未知Lipschitz连续系统的数据驱动模型无效
Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction
论文作者
论文摘要
在本文中，我们考虑了Lipschitz连续系统的数据驱动的模型无效问题，在该系统中，只有先前的噪声采样数据可用，而不是给定的数学模型。我们表明，可以使用可行性检查来解决此数据驱动的模型无效问题。 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.最后，我们讨论了减少算法的计算复杂性的几种方法，并通过模拟群体意图识别示例来证明它们的有效性。
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.
