Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections
Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections
复制标题
具有感知缺陷的灰盒反馈控制系统的综合引导对抗场景生成
DOI：
10.1145/3477033
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
N. Ozay
中科院分区：
文献类型：
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作者：
Liren Yang;N. Ozay
在本文中，我们在不完美的信息下使用无内存控制器研究反馈动态系统。主要挑战是用循环中的闭环系统具有潜在的复杂甚至未知的控制器。采用的方法将正在测试的系统视为黑框，我们提出了一种合成的指导方法，该方法利用了当前的植物模型的知识。已知的植物和未知的控制器进一步扩展到合并模型不匹配的方法，并伪造有限时间到达范围的规格的环境系统。
In this paper, we study feedback dynamical systems with memoryless controllers under imperfect information. We develop an algorithm that searches for “adversarial scenarios”, which can be thought of as the strategy for the adversary representing the noise and disturbances, that lead to safety violations. The main challenge is to analyze the closed-loop system's vulnerabilities with a potentially complex or even unknown controller in the loop. As opposed to commonly adopted approaches that treat the system under test as a black-box, we propose a synthesis-guided approach, which leverages the knowledge of a plant model at hand. This hence leads to a way to deal with gray-box systems (i.e., with known plant and unknown controller). Our approach reveals the role of the imperfect information in the violation. Examples show that our approach can find non-trivial scenarios that are difficult to expose by random simulations. This approach is further extended to incorporate model mismatch and to falsify vision-in-the-loop systems against finite-time reach-avoid specifications.
DOI：
10.1109/icmla51294.2020.00042
发表时间：
2020-07
期刊：
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
影响因子：
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作者：
Xiao Wang;Saasha Nair;M. Althoff
通讯作者：
Xiao Wang;Saasha Nair;M. Althoff