Interval estimation for LPV systems applying high order sliding mode techniques

Efimov, Denis; Fridman, Leonid; Raïssi, Tarek; Zolghadri, Ali; Seydou, Ramatou

doi:10.1016/j.automatica.2012.06.073

Cited by 178 publications

(105 citation statements)

References 23 publications

(48 reference statements)

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“…Решение проблемы оценивания состояния для сингулярных систем основано на методе коопера-тивных и монотонных систем [13][14][15][16][17][18]. Некоторые результаты были получены по синтезу экспоненциаль-но устойчивых интервальных наблюдателей для систем с запаздыванием [11,12], и в данной работе ста-вится цель -расширить инструменты построения наблюдателей на класс сингулярных систем ДУ.…”

Section: Introductionunclassified

Interval state estimation for singular differential equation systems with delays

Kharkovskaia¹,

Kremlev²,

Efimov³

2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Section: Introductionunclassified

Interval state estimation for singular differential equation systems with delays

Kharkovskaia¹,

Kremlev²,

Efimov³

2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…Finally create a new state vector by augmenting d(t) from (3), andẽ 1 (t) and e y (t) from (17) and (18), such that  ḋ (t) e 1 (t) e y (t)…”

Section: Assumptionmentioning

confidence: 99%

“…Recent results on LPV based fault detection schemes appears in [15], [16], [17] from work in the EU-funded ADDSAFE project. Recently several sliding mode observer structures have been proposed for LPV systems (see for example [18], [19], [20]). However in all these papers the scheduling parameters are assumed to be perfectly known.…”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

Chandra¹,

Alwi

Edwards

2017

European Journal of Control

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This paper presents a sliding mode fault detection scheme for linear parameter varying (LPV) systems with uncertain or imperfectly measured scheduling parameters. In the majority of LPV systems, it is assumed that the scheduling parameters are exactly known. In reality due to noise or possibly faulty sensors, it is sometimes impossible to have accurate knowledge of the scheduling parameters and a design based on the assumption of perfect knowledge of the scheduling parameters cannot be guaranteed to work well in this situation. This paper proposes a sliding mode observer scheme to reconstruct actuator and sensor faults in a situation where the scheduling parameters are imperfectly known. The efficacy of the approach is demonstrated on simulation data taken from the nonlinear RECONFIGURE benchmark model . One approach to extending linear methods to make them work effectively across the whole plant operating range is to design (linear) observers at select operating points, and then schedule the gains with respect to certain parameters [4]. One of the main difficulties with gain scheduling is the selection of the operating points and how to choose the gains at intermediate points. Furthermore, formally, between the operating points the stability of the observer cannot be guaranteed.A more rigorous alternative approach is based on so-called linear parameter varying (LPV) system designs. In the LPV approach the gains are automatically scheduled with respect to the plant varying parameters. Another advantage is many nonlinear systems can be naturally approximated by LPV systems [13] [20]). However in all these papers the scheduling parameters are assumed to be perfectly known. However in reality this may not be the case: for example inaccurate sensors and/or faults can lead to imperfect knowledge of the parameters. The use of this corrupted information will affect the performance of the observer. The design of Luenberger-like observers in scenarios involving uncertain scheduling parameters has been investigated. In [21], using a Linear matrix inequality (LMI) framework an LPV observer was proposed. Later, a controller and a combined controllerobserver scheme for inexact continuous LPV polytopic systems was presented in [22], [23]. In [24], [25] H ∞ filters were employed to deal with uncertainty in the scheduling parameters; exploiting parameter dependent LMI methods. More recently, an H ∞ filter has been proposed to deal with both additive and multiplicative uncertainties in the LPV parameters in [26]. Recent applications of these ideas to aerospace systems have been explored in [27], [28]. A fault reconstruction scheme for LPV systems with perfect scheduling parameter knowledge, using H ∞ methods has been investigated in [29]. Very few papers have addressed specifically the fault reconstruction problem for LPV systems with uncertain scheduling parameters, notable exceptions are [30]. To the authors' knowledge,

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“…This paper continues the framework of interval observer design based on the monotone system theory [3], [18]. Such an approach has been recently extended in [22] to nonlinear systems using a LPV representation with known minorant and majorant matrices, in [21] for observable nonlinear systems and in [8] for a combined application of interval and sliding-mode observers. One of the most complex assumptions for the interval observer design, dealing with cooperativity of the interval estimation error dynamics, was relaxed in [16], [21].…”

Section: Introductionmentioning

confidence: 99%

On set-membership observer design for a class of periodical time-varying systems

Efimov

Raïssi

Chebotarev

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Interval estimation for LPV systems applying high order sliding mode techniques

Cited by 178 publications

References 23 publications

Interval state estimation for singular differential equation systems with delays

Interval state estimation for singular differential equation systems with delays

Fault detection in uncertain LPV systems with imperfect scheduling parameter using sliding mode observers

On set-membership observer design for a class of periodical time-varying systems

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