论文标题
Beta残差：通过贝叶斯推断和精确学习改善感觉故障的耐故障控制
Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning
论文作者
论文摘要
基于模型的故障控制（FTC）通常由两个不同的步骤组成：故障检测和隔离（FDI）和故障适应。在这项工作中，我们调查了摆耐断层控制作为单个贝叶斯推断问题的。先前的工作表明，精确学习允许随机FTC无明确的故障检测步骤。尽管这会导致隐式故障恢复，但未提供有关传感器故障的信息，这对于触发其他影响降低措施可能至关重要。在本文中，我们引入了一种基于精确学习的贝叶斯FTC方法和一种新型的beta残差用于故障检测。提出了仿真结果，支持使用Beta残差与竞争方法使用。
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.
