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Model-based fault-tolerant control (FTC) often consists of two distinctsteps: fault detection & isolation (FDI), and fault accommodation. In this workwe investigate posing fault-tolerant control as a single Bayesian inferenceproblem. Previous work showed that precision learning allows for stochastic FTCwithout an explicit fault detection step. While this leads to implicit faultrecovery, information on sensor faults is not provided, which may be essentialfor triggering other impact-mitigation actions. In this paper, we introduce aprecision-learning based Bayesian FTC approach and a novel beta residual forfault detection. Simulation results are presented, supporting the use of betaresidual against competing approaches.

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