Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Mohamed Baioumy, William Hartemink, Riccardo M.G. Ferrari, Nick Hawes

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

Model-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 novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.

Original languageEnglish
Pages (from-to)285-291
JournalIFAC-PapersOnline
Volume55
Issue number6
DOIs
Publication statusPublished - 2022
Event11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2022 - Pafos, Cyprus
Duration: 8 Jun 202210 Jun 2022

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