文章基本信息

标题：Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
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作者：Mohamed Baioumy ; William Hartemink ; Riccardo M.G. Ferrari 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：6
页码：285-291
DOI：10.1016/j.ifacol.2022.07.143
语种：English
出版社：Elsevier
摘要：AbstractModel-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novelbeta residualfor fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.