Stochastic Model Predictive Control for Gust Alleviation during Aircraft Carrier Landing

Gaurav Misra, Xiaoli Bai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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