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Abstract
Flight testing of explicit rotor-state feedback (RSF) fly-by-wire control laws showed that measuring rotor tip-path-plane (TPP) flapping, via a laser measurement system, provided additional lead to the control system. This resulted in superior handling qualities in turbulence and heavy winds and improved stability margins. However, a significant impediment to the adoption of explicitly measured RSF has been the difficulty in extracting reliable rotor measurements. Therefore, this paper describes the development of a Kalman filter that was designed to estimate rotor TPP coordinates, and remove noise from the flapping signals while retaining the useful information without introducing large time delay, as would be the case for conventional low pass filtering. A new method for the design of the process noise covariance matrix using optimization of frequency domain specifications was implemented using flight test data from the UH-60 Black Hawk. The design was integrated into an explicit rotor-state feedback control algorithm, where it was tested for robustness to sensor faults and effectiveness based on improvements to stability margins. The results showed that the Kalman filter was robust to rotor blade sensor spike and drop-out faults and resulted in improved stability margins and handling qualities.

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  Kalman Filter Estimation of Rotor-State Flapping: An Optimization-based Approach with UH-60 Flight Test Data

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