Fault Detection and Fault-Tolerant Control Using Sliding Modes

Alwi, Halim; Edwards, Christopher; Tan, Chee Pin

doi:10.1007/978-0-85729-650-4

Cited by 280 publications

(317 citation statements)

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“…In this paper it will be assumed that typically ρ(t) ̸ =ρ(t) and an observer will be presented to estimate the faults despite imperfect knowledge of the scheduling parameters. The estimate of the fault f i (t) will be obtained by scaling the so-called equivalent injection signal [11] (whilst sliding). Specificallyf…”

Section: Sliding Mode Observer For Uncertain Linear Parameter Vamentioning

confidence: 99%

“…where W ∈ R q×p is design freedom and ν eq represent the equivalent injection (the 'average' value of ν(t)) required to maintain sliding [11]. Define the state estimation error as e(t) =x(t) − x(t) and partition the error conformably with the partitions in (5) and (6) so that e(t) = col(e 1 (t), e 2 (t)).…”

Section: Sliding Mode Observer For Uncertain Linear Parameter Vamentioning

confidence: 99%

“…One popular approach for designing FDI schemes is based on observer concepts, usually based on linear theory or slightly extended versions. A wide range of observer paradigms have been considered in the literature: for example Kalman filters and their extensions (see for example [1], [2], [3]), H ∞ filters (see for example [4], [5], [6]), unknown input observers (see for example [7], [8], [9]), and sliding mode observers (SMOs) (see for example [10], [11], [12]). 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].…”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

“…This paper presents a new FDI scheme based on sliding mode ideas for LPV systems with uncertain scheduling parameters. This exploits the fact that sliding mode observers have an inherent ability for fault reconstruction, due to the unique property of the equivalent injection signal [11]. This work is a direct extension of [20], where it was assumed that the scheduling parameters are perfectly measurable.…”

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

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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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Section: Sliding Mode Observer For Uncertain Linear Parameter Vamentioning

confidence: 99%

Section: Sliding Mode Observer For Uncertain Linear Parameter Vamentioning

confidence: 99%

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

Section: Introduction Fault Detection and Isolation (Fdi)mentioning

confidence: 99%

See 2 more Smart Citations

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

Chandra¹,

Alwi

Edwards

2017

European Journal of Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…4) Es wird also zum Beispiel jedes Element des Vektors f A mit dem gesamten Zustandsvektor x multipliziert, Somit lassen sich multiplikative Fehler als additive Fehler darstellen. Dabei ist jedoch zu beachten, dass multiplikative Fehler im Gegensatz zu additiven Fehlern die Stabilität des Systems beeinflussen können.…”

Section: Typen Von Fehlernunclassified

Entwurf robuster modellbasierter Fehlerisolationsfilter

Wahrburg

2014

At - Automatisierungstechnik

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Active fault‐tolerant control system design with trajectory re‐planning against actuator faults and saturation: Application to a quadrotor unmanned aerial vehicle

Chamseddine

Theilliol

Zhang

et al. 2013

Adaptive Control & Signal

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During the past 30 years, various fault-tolerant control (FTC) methods have been developed to address actuator or component faults for various systems with or without tracking control objectives. However, very few FTC strategies establish a relation between the post-fault reference trajectory to track and the remaining resources in the system after fault occurrence. This is an open problem that is not well considered in the literature. The main contribution of this paper is in the design of a reconfigurable FTC and trajectory planning scheme with emphasis on online decision making using differential flatness. In the fault-free case and on the basis of the available actuator resources, the reference trajectories are synthesized so as to drive the system as fast as possible to its desired setpoint without violating system constraints. In the fault case, the proposed active FTC system (AFTCS) consists in synthesizing a reconfigurable feedback control along with a modified reference trajectories once an actuator fault has been diagnosed by a fault detection and diagnosis scheme, which uses a parameter-estimation-based unscented Kalman filter. Benefited with the integration of trajectory re-planning using the flatness concept and the compensation-based reconfigurable controller, both faults and saturation in actuators can be handled effectively with the proposed AFTCS design. Advantages and limitations of the proposed AFTCS are illustrated using an experimental quadrotor unmanned aerial vehicle testbed.The reference trajectories F are determined as function of the initial conditions of the system and the final desired conditions as well as the initial time t 0 and the final time t f . The initial and final conditions and the initial time t 0 are known, and the sole unknown is the final time t f . Generally speaking, it is required to attain the desired objectives as fast as possible while respecting system When complete loss of actuators are not considered (i.e., w i < 1 or i > 0), 1 is invertible and 1 u > u and 1 u > u. Thus, for the remaining trajectories F rem .t / to travel, there is no AFTCS AND TRAJECTORY DESIGN AGAINST ACTUATOR FAULTS AND SATURATION P x f .t / D Ax f .t / C B.I m m W /u F T C .t / D Ax f .t / C B.I m m W /.u nom .t / C u add .t // D Ax f .t / C Bu nom .t / BW u nom .t / C B.I m m W /u add .t / (32)

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Fault Detection and Fault-Tolerant Control Using Sliding Modes

Cited by 280 publications

References 0 publications

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

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

Entwurf robuster modellbasierter Fehlerisolationsfilter

Active fault‐tolerant control system design with trajectory re‐planning against actuator faults and saturation: Application to a quadrotor unmanned aerial vehicle

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