This paper deals with the problem of steering an aircraft along a reference trajectory while counteracting the wind disturbance. We develop a control strategy where the aircraft nonlinear dynamics, physical limitations on the aircraft maneuverability, and passengers comfort are accounted for by feedback linearization and a suitable convex relaxation of constraints. A probabilistic constraint is introduced to account for the tracking error introduced by the stochastic wind disturbance. Since wind is represented by a Gaussian random field and its characteristics depend on both time and space, we identify on-the-fly a local autoregressive model via recursive least squares with forgetting factor. The probabilistic constraint formulation, the wind model update, and the re-computation of the control action jointly allow to account for the spatial variability of the random field and to obtain recursive feasibility in the receding horizon solution. A randomized method is adopted to obtain a convex relaxation of the resulting chance-constrained optimization problem, which can then be solved on-line, at low computational effort.

A stochastic strategy integrating wind compensation for trajectory tracking in aircraft motion control

DEORI, LUCA;GARATTI, SIMONE;PRANDINI, MARIA
2016-01-01

Abstract

This paper deals with the problem of steering an aircraft along a reference trajectory while counteracting the wind disturbance. We develop a control strategy where the aircraft nonlinear dynamics, physical limitations on the aircraft maneuverability, and passengers comfort are accounted for by feedback linearization and a suitable convex relaxation of constraints. A probabilistic constraint is introduced to account for the tracking error introduced by the stochastic wind disturbance. Since wind is represented by a Gaussian random field and its characteristics depend on both time and space, we identify on-the-fly a local autoregressive model via recursive least squares with forgetting factor. The probabilistic constraint formulation, the wind model update, and the re-computation of the control action jointly allow to account for the spatial variability of the random field and to obtain recursive feasibility in the receding horizon solution. A randomized method is adopted to obtain a convex relaxation of the resulting chance-constrained optimization problem, which can then be solved on-line, at low computational effort.
2016
Proceedings of the 2016 IEEE 55th Conference on Decision and Control, CDC 2016
978-150901837-6
Artificial Intelligence; Decision Sciences (miscellaneous); Control and Optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1009684
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