Stochastic Model Predictive Control for Gust Alleviation during Aircraft Carrier Landing

被引:0
|
作者
Misra, Gaurav [1 ]
Bai, Xiaoli [1 ]
机构
[1] Rutgers State Univ, Dept Mech & Aerosp Engn, Piscataway, NJ 08854 USA
来源
2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC) | 2018年
关键词
SYSTEM; STATE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:1479 / 1484
页数:6
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