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 language | English |
---|---|
Pages (from-to) | 285-291 |
Journal | IFAC-PapersOnline |
Volume | 55 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2022 |
Event | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2022 - Pafos, Cyprus Duration: 8 Jun 2022 → 10 Jun 2022 |