Abstract

A new diagnosis method of Lurie system stability by using Chaotic time series data was proposed in order to solve stability analysis of flight control system with fault and uncertain. Firstly, the reasons for the instability of the nonlinear system of Ruri leaf was analyzed by using the small gain theory and linear matrix inequalities in the robust control theory. And the stability conditions of Rui leaf system under the condition of uncertainty and failure were proved theoretically. In order to quantify the stability of nonlinear systems in complex Ruri leaves, based on the theory of phase space reconstruction, the nonlinear Ruri system with continuous discrete characteristics was converted into an approximate time discrete equation, mapping it to a low dimensional primitive space, by introducing Q Gauss function into Kernel function, the generalization ability of neural networks are enhanced, realizing the stability analysis method based on the characteristic of primitive migration, which is suitable for various fault and uncertainty conditions, and the relative quantized stability norm can be given. The simulation shows that the present method can effectively solve the stability analysis and determination of flight control system under various factors.

Highlights

  • A new diagnosis method of Lurie system stability by using Chaotic time series data was proposed in order to solve stability analysis of flight control system

  • the reasons for the instability of the nonlinear system of Ruri leaf was analyzed by using the small gain theory

  • In order to quantify the stability of nonlinear systems in complex Ruri leaves

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Summary

Introduction

ÌïẊ ( t) = ( A′′( t) + Δ􀭾A( t) ) X 统模型 ìïïẊ = A( X) + ΩB( U) + ΔF( X,U) + ΔFδ( X,U) íY = ΘC( X) + ΔFD( X) îïïΔF′ = ΔF′A + ΔF′δ - KΔFD = DFΔF[ r( t) ] EF X( k + 1) = faTp[ x( k) ,u( k) ] 则有以下引理成立: ( 15) 控制器 u( k) = uc( x( k) ) , 如果闭环近似模型(32) 式全局指数稳定,则有: 1) 存在函数 ρ ∈ k∞ ,常数 T1∗ > 0,当 T ∈ (0, T1∗) 时,对任意 x ∈ Rn ,近似模型的误差满足 ‖FeTx( x,uT( x) ) - FaTp( x,uT( x) ) ‖ ≤ Tρ( T) ‖x‖

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