Facilitating Autonomous Systems with AI-Based Fault Tolerance and Computational Resource Economy
Facilitating Autonomous Systems with AI-Based Fault Tolerance and Computational Resource Economy
复制标题
通过基于人工智能的容错和计算资源经济促进自治系统
DOI：
10.3390/electronics9050788
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发表时间：
2020
期刊：
影响因子：
2.9
通讯作者：
A. Zolotas
中科院分区：
文献类型：
--
作者：
K. M. Deliparaschos;K. Michail;A. Zolotas
提出的是在自治系统的容错能力，特别考虑到低计算复杂性和系统接口设备（传感器/执行器）的性能的便利。传统的基于模型的容错/检测单元的多个传感器故障自动化需要一个银行的估计，通常卡尔曼为基础的。提出了一种基于人工智能的控制框架，使低计算能力的容错。与估计器库方法相反，所提出的框架展示了用于多个执行器/传感器故障检测的单个单元。所提出的方案的有效性示出通过严格的分析几个传感器故障的情况下，电磁悬浮试验台。
Proposed is the facilitation of fault-tolerant capability in autonomous systems with particular consideration of low computational complexity and system interface devices (sensor/actuator) performance. Traditionally model-based fault-tolerant/detection units for multiple sensor faults in automation require a bank of estimators, normally Kalman-based ones. An AI-based control framework enabling low computational power fault tolerance is presented. Contrary to the bank-of-estimators approach, the proposed framework exhibits a single unit for multiple actuator/sensor fault detection. The efficacy of the proposed scheme is shown via rigorous analysis for several sensor fault scenarios for an electro-magnetic suspension testbed.