Validating an online adaptive system using SVDD

One of the goals of verification and validation (V&V) activities for online adaptive control systems is providing assurance that they are able to detect novel system behaviors and provide adequate (safe) control actions. Novel (or abnormal) system behaviors cannot be enumerated or fully and explicitly described in requirements documentation. Therefore, they have to be observed and recognized during the operation. Novelty detection methods, therefore, provide an adequate approach for the V&V purposes. We propose a novelty detection method based on support sector data description (SVDD) as a candidate approach for validating adaptive control systems. As a one-class classifier, the support vector data description is able to form a decision boundary around the learned data domain with very little or no knowledge of data points outside the boundary (outliers). We apply the SVDD techniques for novelty detection as part of the validation on an intelligent flight control system (IFCS). Experimental results show that the SVDD can be adopted as an effective tool for finding indications of the safe region for the learned domain, whereby we are able to separate faulty behavior from normal events.