Michael Elgersma

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We present a versatile statistical verification methodology and we illustrate different uses of this methodology on two examples of nonlinear real-time UAV controllers. The first example applies our statistical methodology to the verification of a computation time property for a software implementation of a high-performance controller as a function of(More)
Worldwide air traffic levels are growing at a rate expected to double the current traffic level by 2020. The current technology Air Traffic Control systems are stretched to their limit and are prone to large delays during the peak summer travel season. There is doubt that the current systems can be scaled up to meet the expected demand levels. Many Air(More)
We present an approach to verifying the performance of an intelligent control algorithm for which traditional, deterministic verification is not feasible. Our approach is based on statistical learning theory. We develop a classifier based on simulation data to partition the potential operating region of a system under control (here an autonomous helicopter)(More)
This paper describes development and performance analysis of an active failure management system for the commuter and business aircraft control recovery. System failures are detected and isolated using a hierarchy of techniques that is chosen to ensure minimal disruption of operations and to minimize the number of false alarms. Successive layers in the(More)
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