Abstract
An innovative use of the Extended Kalman Filter (EKF) is proposed to perform automatic take off and landing by the rejection of disturbances due to turbulence.
By using two simultaneously working Extended Kalman Filters, a procedure is implemented: the first filter, by using measurements gathered in turbulent air, estimates wind components; the second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances.
The fundamental innovation of such a procedure consists in the fact that the covariance matrices of process (Q) and measurement (R) noise are not treated as filter design parameters. In this way determined optimal values of the aforementioned matrices lead to robust control laws.
At any moment, during the estimation process, the EKF employs the optimal values of Q and R. To determine these ones, adequate constrains, related to flight path characteristics, are inserted into the algorithm.
In particular, to determine wind components, the constrains are imposed to elevation, altitude and longitudinal position; whereas, to determine control actions, the constrains are imposed to an adequate performance index obtained by using measurements gathered by a small set of sensors (IMU, air data boom and a low rate GPS) in turbulent air.
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Grillo, C., Montano, F. (2020). Automatic Take Off and Landing for UAS Flying in Turbulent Air - An EKF Based Procedure. In: Mazal, J., Fagiolini, A., Vasik, P. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2019. Lecture Notes in Computer Science(), vol 11995. Springer, Cham. https://doi.org/10.1007/978-3-030-43890-6_10
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DOI: https://doi.org/10.1007/978-3-030-43890-6_10
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