TY - GEN
T1 - Stochastic Model Predictive Control for Gust Alleviation during Aircraft Carrier Landing
AU - Misra, Gaurav
AU - Bai, Xiaoli
N1 - Funding Information:
*This work was supported through the Office of Naval Research (ONR) grant N00014-16-1-2729.
Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - This paper presents a constrained stochastic model predictive control approach for approach and landing on an aircraft carrier. Particularly, we analyze the offset recovery control for an aircraft during the powered approach-to-landing phase commonly associated with carrier based landings in the presence of stochastic wind gusts. A Dryden turbulence model is used to model the gust wind. An augmented stochastic linear time invariant system trimmed at a nominal flight condition is constructed with the gust appearing as an input. Probabilistic constraints are introduced to account for the state and control bounds. An affine disturbance feedback based control is proposed for offset recovery and glideslope regulation. This formulation leads to a tractable, sub-optimal convex approximation of the original stochastic problem and is amenable to fast online optimization solvers. The performance of the proposed approach is evaluated with numerical simulations.
AB - This paper presents a constrained stochastic model predictive control approach for approach and landing on an aircraft carrier. Particularly, we analyze the offset recovery control for an aircraft during the powered approach-to-landing phase commonly associated with carrier based landings in the presence of stochastic wind gusts. A Dryden turbulence model is used to model the gust wind. An augmented stochastic linear time invariant system trimmed at a nominal flight condition is constructed with the gust appearing as an input. Probabilistic constraints are introduced to account for the state and control bounds. An affine disturbance feedback based control is proposed for offset recovery and glideslope regulation. This formulation leads to a tractable, sub-optimal convex approximation of the original stochastic problem and is amenable to fast online optimization solvers. The performance of the proposed approach is evaluated with numerical simulations.
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U2 - 10.23919/ACC.2018.8431815
DO - 10.23919/ACC.2018.8431815
M3 - Conference contribution
AN - SCOPUS:85052577487
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 1479
EP - 1484
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -