Backstepping control strategy for aero-engine using neural networks

PAN Mu-xuan; HUANG Jin-quan; YIN Shi

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Backstepping control strategy for aero-engine using neural networks

更新时间：2026-07-07

- Backstepping control strategy for aero-engine using neural networks
- Vol. 24, Issue 10, (2009)
- 作者机构：
  
  南京航空航天大学能源与动力学院,南京,210016
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2009
- 稿件说明：
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PAN Mu-xuan, HUANG Jin-quan, YIN Shi. Backstepping control strategy for aero-engine using neural networks[J]. 2009, 24(10).
DOI：

PAN Mu-xuan, HUANG Jin-quan, YIN Shi. Backstepping control strategy for aero-engine using neural networks[J]. 2009, 24(10). DOI：

摘要

针对航空发动机非线性和不确定性的特点

提出了一种基于神经网络的反步控制方法.采用径向基神经网络估计未知系统方程

并用一种平滑切换法有效避免了控制器奇异问题.反步法的设计基于Lya-punov稳定性原理

保证了闭环系统一致渐近有界.最后针对某型涡扇发动机非线性模型设计了高压转速控制器

仿真结果验证了该方法的有效性.

Abstract

A Backstepping control strategy based on neural network was presented in view of nonlinearity and uncertainty of aero-engine.Radial basis function(RBF) neural network was used to estimate the equations of the unknown system

and a smooth-switching algorithm was proposed to avoid singularity phenomenon.Using Lyapunov stability analysis

the uniformly ultimately boundedness of closed-loop systems was proven.Finally

the compressor speed controller was designed based on a nonlinear model of some turbofan engine.The simulation results illustrate the effectiveness of the proposed approach.

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Related Institution

南京航空航天大学能源与动力学院江苏省航空动力系统重点实验室

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