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Gain self-scheduled H_∞ control for morphing aircraft in the wing transition process based on an LPV model

Gain self-scheduled H_∞ control for morphing aircraft in the wing transition process based on an LPV model

作     者:Yue Ting Wang Lixin Ai Junqiang 

作者机构:School of Aeronautic Science and Engineering Beihang University The First Aircraft Design Institute of AVIC 

出 版 物:《Chinese Journal of Aeronautics》 (中国航空学报(英文版))

年 卷 期:2013年第26卷第4期

页      面:909-917页

核心收录:

学科分类:080103[工学-流体力学] 08[工学] 080104[工学-工程力学] 0801[工学-力学(可授工学、理学学位)] 

基  金:co-supported by China Postdoctoral Science Foundation(Nos.20110490259 2012T50038) 

主  题:Gain self scheduled H 1 robust control Linear parameter varying Morphing aircraft Wing transition 

摘      要:This article investigates gain self-scheduled H 1 robust control system design for a tailless fold- ing-wing morphing aircraft in the wing shape varying process. During the wing morphing phase, the aircraft's dynamic response will be governed by time-varying aerodynamic forces and moments. Nonlinear dynamic equations of the morphing aircraft are linearized by using Jacobian linearization approach, and a linear parameter varying (LPV) model of the morphing aircraft in wing folding is obtained. A multi-loop controller for the morphing aircraft is formulated to guarantee stability for the wing shape transition process. The proposed controller uses a set of inner-loop gains to provide stability using classical techniques, whereas a gain self-scheduled H 1 outer-loop controller is devised to guarantee a specific level of robust stability and performance for the time-varying dynamics. The closed-loop simulations show that speed and altitude vary slightly during the whole wing folding process, and they converge rapidly after the process ends. This proves that the gain self-scheduled H 1 robust controller can guarantee a satisfactory dynamic performance for the morphing aircraft during the whole wing shape transition process. Finally, the flight control system's robustness for the wing folding process is verified according to uncertainties of the aerodynamic parameters in the nonlinear model.

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