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arXiv 2206.08069eess.SYcs.SY
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基于数据驱动抽象的控制综合

Data-Driven Abstraction-Based Control Synthesis

Milad Kazemi, Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani, Ben Wooding

摘要:

本文研究动态未知的连续空间系统的控制器形式化综合,以满足线性时序逻辑公式的要求。基于形式抽象的综合方案依赖于系统的精确数学模型来构建有限抽象模型,然后将其用于设计控制器。基于抽象的计划是不适用的动态系统是未知的。我们提出了一个数据驱动的方法,计算使用有限数量的轨迹系统的增长界限。结合在一起的采样轨迹的增长,然后用于构建的抽象和合成控制器。我们的方法将增长边界的计算作为一个鲁棒的凸优化程序(RCP)。由于未知的动态出现在优化,我们制定了一个场景凸规划(SCP)对应的RCP使用有限数量的采样轨迹。我们建立了一个样本复杂度的结果,给出了一个下界的采样轨迹的数量,以保证增长界计算的SCP与给定的信心的正确性。我们还提供了一个样本的复杂性结果的满意度的规格上的系统在闭环控制器的设计为一个给定的信心。我们的研究结果是建立在估计系统的Lipschitz常数上的界,并提供有限和无限时域规格的满意保证。我们表明,我们的数据驱动的方法可以很容易地用作一个无模型的抽象细化方案,通过修改制定的增长界限,并提供类似的样本复杂性的结果。我们的方法的性能显示在三个案例研究。

英文摘要:

This paper studies formal synthesis of controllers for continuous-space systems with unknown dynamics to satisfy requirements expressed as linear temporal logic formulas. Formal abstraction-based synthesis schemes rely on a precise mathematical model of the system to build a finite abstract model, which is then used to design a controller. The abstraction-based schemes are not applicable when the dynamics of the system are unknown. We propose a data-driven approach that computes the growth bound of the system using a finite number of trajectories. The growth bound together with the sampled trajectories are then used to construct the abstraction and synthesise a controller. Our approach casts the computation of the growth bound as a robust convex optimisation program (RCP). Since the unknown dynamics appear in the optimisation, we formulate a scenario convex program (SCP) corresponding to the RCP using a finite number of sampled trajectories. We establish a sample complexity result that gives a lower bound for the number of sampled trajectories to guarantee the correctness of the growth bound computed from the SCP with a given confidence. We also provide a sample complexity result for the satisfaction of the specification on the system in closed loop with the designed controller for a given confidence. Our results are founded on estimating a bound on the Lipschitz constant of the system and provide guarantees on satisfaction of both finite and infinite-horizon specifications. We show that our data-driven approach can be readily used as a model-free abstraction refinement scheme by modifying the formulation of the growth bound and providing similar sample complexity results. The performance of our approach is shown on three case studies.

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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