ベータ残差：ベイズ推論と精度学習によるセンサ故障に対する耐故障制御の改善
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
耐故障制御を単一のベイズ推論問題として定式化し、精度学習と新規のベータ残差を組み合わせてセンサ故障の検出と暗黙的な復旧を両立させる手法を提案した。
著者: Mohamed Baioumy, William Hartemink, Riccardo M. G. Ferrari, Nick Hawes
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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.
