Model-Free Neural Fault Detection and Isolation for Safe Control
Model-Free Neural Fault Detection and Isolation for Safe Control
复制标题
DOI：
10.1109/lcsys.2023.3302768
复制
发表时间：
2023
影响因子：
3
通讯作者：
Kunal Garg;Charles Dawson;Kathleen Xu;Melkior Ornik;Chuchu Fan
中科院分区：
文献类型：
--
作者：
Kunal Garg;Charles Dawson;Kathleen Xu;Melkior Ornik;Chuchu Fan
在安全关键系统中，突然发生的执行器故障会导致安全违规并导致严重后果。现有的容错控制（FTC）方法通常侧重于维护系统的性能，而不考虑系统的安全性。控制障碍函数（CBF）已经成为控制理论中为控制系统提供安全保证的有用工具。然而，CBF的现有应用要么不考虑致动器故障，要么只考虑已知哪个致动器有故障的特殊情况，或者存在冗余致动器以即使在故障和失效下也保持可控性的情况。在这封信中，我们解决了一个更现实的情况下，它是完全未知的致动器故障，当故障发生的安全恢复的问题。我们开发了一种新的无模型学习框架，用于基于输出的神经故障检测器，该检测器可以检测何时发生故障以及执行器。基于学习的功能，我们提出了一个自动检测和故障恢复的切换框架。我们评估我们的方法的案例研究涉及Crazyflie四旋翼电机故障。
A sudden actuator fault in a safety-critical system can cause safety violations and lead to severe consequences. Existing fault-tolerant control (FTC) approaches normally focus on maintaining system performance and do not consider system safety. Control Barrier Functions (CBFs) have emerged as useful tools from control theory for providing safety guarantees for control systems. However, existing applications of CBFs either do not consider actuator faults or only consider the special case where it is known which actuator is faulty or the case when redundant actuators are present to maintain controllability even under faults and failures. In this letter, we address the problem of safe recovery under a more realistic scenario where it is completely unknown which actuator is faulty and when the fault occurs. We develop a novel model-free learning framework for an output-based neural fault-detector that detects when a fault occurs and in which actuator. Based on the learned functions, we propose a switching framework for automatically detecting and recovering from faults. We evaluate our method on a case study involving a Crazyflie quadrotor with a motor failure.