Backstepping sliding mode neural network control system for hypersonic vehicle

Rong LIU; Da-qing HUANG; Ding-guo JIANG

doi:10.3788/OPE.20192711.2392

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Backstepping sliding mode neural network control system for hypersonic vehicle

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-08-13

- Backstepping sliding mode neural network control system for hypersonic vehicle
- Optics and Precision Engineering Vol. 27, Issue 11, Pages: 2392-2401(2019)
- 作者机构：
  
  1.南京航空航天大学无人机研究院，江苏南京 210016
  2.中国人民解放军第5311工厂，江苏南京 210000
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192711.2392
  CLC： TG156
- Received：21 March 2019，
  
  Accepted：20 May 2019，
  
  Published：15 November 2019
- 稿件说明：
移动端阅览
Rong LIU, Da-qing HUANG, Ding-guo JIANG. Backstepping sliding mode neural network control system for hypersonic vehicle[J]. Optics and precision engineering, 2019, 27(11): 2392-2401.
DOI：

Rong LIU, Da-qing HUANG, Ding-guo JIANG. Backstepping sliding mode neural network control system for hypersonic vehicle[J]. Optics and precision engineering, 2019, 27(11): 2392-2401. DOI： 10.3788/OPE.20192711.2392.

摘要

针对高超声速飞行器一体化气动布局导致弹性机体与推进系统间的强耦合性，以及跨大空域及高速飞行过程中导致气动特性存在强非线性、不确定性和明显的时变特性，提出一种基于小脑神经网络的高超声速飞行器反步滑模控制策略。首先建立高超声速飞行器纵向非线性数学模型，并采用输入-输出反馈线性化方法，解除多变量之间的耦合关系；然后设计基于反步法的滑模变结构控制器解决系统非匹配不确定性难题；同时为弥补反步滑模控制器鲁棒性不足缺点，利用自回归小脑神经网络(RCMAC)的在线非线性逼近、自学习能力和相应控制结构，设计基于RCMAC的反步滑模控制器。仿真试验结果表明，该方法下高超声速飞行器纵向的高度控制精度可达到0.5 m，速度控制精度为0.1 m/s，可以保证闭环系统全局稳定，且拥有良好的跟踪性能和鲁棒性能。

Abstract

Strong coupling between the elastic body and the propulsion system in a hypersonic vehicle is caused by its integrated pneumatic layout and strong nonlinearity uncertainty

and obvious time-varying characteristics of aerodynamics

when the vehicle spans a large airspace and is flying at high speed. To eliminate the influence of this coupling

we propose a backstepping sliding mode control scheme based on a recurrent cerebellar model articulation controller (RCMAC). The input-output feedback linearization approach is used to resolve coupling between multiple variables. Firstly

we established the nonlinear mathematical longitudinal model of a hypersonic vehicle. Secondly

the sliding mode variable structure controller was designed to do away with the uncertainty of mismatch. Finally

the RCMAC-based backstepping sliding mode controller was designed. The controller makes up for the shortcoming of robustness of the hypersonic vehicle by its control structure and ability of nonlinear approximation and self-learning. The results of the simulation experiment indicate that the longitudinal altitude and velocity control precisions of a hypersonic vehicle can reach 0.5 m and 0.1 m/s

respectively and can therefore satisfy the system requirements of global stability

good dynamic responses

and robustness.

关键词

Keywords

references

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