空间可变翼飞行器小翼伸缩自适应滑模控制

Adaptive sliding mode control for the telescopic winglet of a nearspace morphing vehicle

近空间可变翼飞行器具有可伸缩小翼结构,针对小翼伸出和收回状态变化过程中,存在参数摄动,可能造成飞行器状态以及控制量的大范围跳变,影响飞行器的稳定性的问题,本文提出一种快速双幂次趋近律滑模与神经网络结合的自适应滑模控制方法。应用该方法设计快速双幂次趋近律滑模控制器,并利用神经网络充分逼近复杂的非线性关系能力,得到小翼伸缩全过程的滑模趋近律。对比分析传统滑模控制方法和快速双幂次滑模与神经网络结合自适应控制效果,仿真结果表明快速双幂次滑模控制与神经网络结合方法具有良好的控制效果。

Nearspace morphing vehicles possess telescopic winglets. Aircraft stability may be affected by parameter perturbations, which is caused by the large-range hop to the state and the control volume of the aircraft during the stretching and withdrawal of the winglet. An adaptive sliding mode control method that combines fast double-power reaching law sliding mode with a neural network is proposed in this paper. A sliding mode controller is designed on the basis of the fast double-power reaching law. The neural network is used to model complex nonlinear relationships to obtain the reaching law of the sliding mode throughout the entirety of the stretching and withdrawal processes of the winglet. The proposed method is compared with the traditional sliding mode control method. The simula- tion results showed that the proposed method has an excellent control effect.