Nonstationary Random Parametric Vibration in Light Aircraft Landing Gear

Huntington, D. E.; Lyrintzis, Constantinos S.

doi:10.2514/2.2272

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…These disturbances generally degrade the performance of the systems. For example, random vibration of the aircraft system, even in light aircraft, is important because random vibration analysis is needed to conduct accurate fatigue analysis and affect the design of control systems (Huntington and Lyrintzis 1998), so that accurate fatigue life may be computed, and the aircraft design may be changed early and inexpensively if needed. Then, we let the motion of the F-18 aircraft be determined by the system of stochastic differential equations.…”

Section: Examplementioning

confidence: 99%

Dynamic output feedback control synthesis for stochastic time-delay systems

Li¹,

Yang²

2012

International Journal of Systems Science

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This article deals with the dynamic output feedback control synthesis problem for Itoˆ-type stochastic time-delay systems. Our aim is to design a full order dynamic output feedback controller to achieve the desired control objectives. We will formulate the controller design problem as an H 1 optimisation problem in the mean-square sense. The main contributions of this article are as follows: (i) for stochastic systems, the design of a controller with multiple objectives can be addressed without employing a unique Lyapunov function; (ii) using an inequality technique and Finsler Lemma, we provide convex controller synthesis conditions described by linear matrix inequalities (LMIs). Some examples are presented to show the effectiveness of the developed theoretical results.

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Section: Examplementioning

confidence: 99%

Dynamic output feedback control synthesis for stochastic time-delay systems

Li¹,

Yang²

2012

International Journal of Systems Science

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“…These perturbations generally degrade the performance of the system and, in some cases, may even jeopardize the outcome of the engineering task. For example, random vibration of aircraft engine system, even in light aircraft, is important because random vibration analysis is needed to conduct accurate fatigue analysis and affect the design of engine control systems 44, so that the accurate fatigue life may be computed, and the engine design may be changed early and inexpensively if needed. As in 45, let the motion of the F‐404 aircraft engine be determined by the system of stochastic differential equations derived from the basic aerodynamics, and the stochastic part of the motion is due to the changing wind.…”

Section: Illustrative Examplesmentioning

confidence: 99%

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Shen

Wang

Shu

et al. 2010

Intl J Robust & Nonlinear

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SUMMARYThis paper is concerned with the robust H ∞ finite-horizon filtering problem for discrete time-varying stochastic systems with multiple randomly occurred sector-nonlinearities (MROSNs) and successive packet dropouts. MROSNs are proposed to model a class of sector-like nonlinearities that occur according to the multiple Bernoulli distributed white sequences with a known conditional probability. Different from traditional approaches, in this paper, a time-varying filter is designed directly for the addressed system without resorting to the augmentation of system states and measurement, which helps reduce the filter order. A new H ∞ filtering technique is developed by means of a set of recursive linear matrix inequalities that depend on not only the current available state estimate but also the previous measurement, therefore ensuring a better accuracy. Finally, two illustrative examples are used to demonstrate the effectiveness and applicability of the proposed filter design scheme.

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“…In this example, we assume the H ∞ disturbance attenuation level γ = 1.2. By using Matlab LMI Control Toolbox to solve the LMI in (30), we can obtain the solutions of P 11 , P 21 , P 12 , P 22 , Q, W 1 , W 2 , Z 1 , Z 2 , ε 11 , ε 21 , ε 12 The simulation result of the state response of the real states x(t) and their estimatesx(t) are displayed in Fig. 1, and Fig.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…In general, these perturbations can degrade the system performance, or even jeopardize the outcome of the engineering task. For example, random vibration of aircraft engine system, even in light aircraft, is important because random vibration analysis is needed to conduct accurate fatigue analysis and affect the design of engine control systems [12], so that the accurate fatigue life may be computed, and the engine design may be changed early and inexpensively if needed. As in [11], let the motion of the F-404 aircraft engine be determined by the system of stochastic differential equations derived from the basic aerodynamics, and the stochastic part of the motion is due to the changing wind.…”

Section: Numerical Examplesmentioning

confidence: 99%

Robust H ∞ Filtering for Uncertain Nonlinear Stochastic Systems with Mode-dependent Time-delays and Markovian Jump Parameters

Yan

Zhang

Shi

et al. 2010

Circuits Syst Signal Process

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This paper investigates the problem of robust H ∞ filtering for a class of uncertain nonlinear stochastic time-delay systems with Markovian jump parameters.The system under consideration contains parameter uncertainties, Itô-type stochastic disturbances, unknown nonlinearities as well as time-varying delays, which vary in a range and dependent on the mode of the operation. We aim to design a full-order 304 Circuits Syst Signal Process (2011) 30: 303-321 Markovian jump filter such that, for all admissible uncertainties, the filtering error system is robustly asymptotically stable in the mean square, and a prescribed H ∞ disturbance attenuation level is guaranteed. By using the Lyapunov stability theory and Itô differential rule, some sufficient conditions in terms of linear matrix inequality (LMI) are proposed to guarantee the existence of the desired H ∞ filter. Then, the explicit expressions of the desired filter parameters are given. Illustrative examples are provided to demonstrate the effectiveness and applicability of the proposed method.

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Nonstationary Random Parametric Vibration in Light Aircraft Landing Gear

Cited by 16 publications

References 14 publications

Dynamic output feedback control synthesis for stochastic time-delay systems

Dynamic output feedback control synthesis for stochastic time-delay systems

H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Robust H ∞ Filtering for Uncertain Nonlinear Stochastic Systems with Mode-dependent Time-delays and Markovian Jump Parameters

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Nonstationary Random Parametric Vibration in Light Aircraft Landing Gear

Cited by 16 publications

References 14 publications

Dynamic output feedback control synthesis for stochastic time-delay systems

Dynamic output feedback control synthesis for stochastic time-delay systems

H∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts

Robust H ∞ Filtering for Uncertain Nonlinear Stochastic Systems with Mode-dependent Time-delays and Markovian Jump Parameters

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H_∞ filtering for uncertain time‐varying systems with multiple randomly occurred nonlinearities and successive packet dropouts