Designing fault detection observers for linear systems with mismatched unknown inputs

Park, Tae-Geon

doi:10.1016/j.jprocont.2013.06.009

Cited by 33 publications

(22 citation statements)

References 23 publications

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“…Owing to the increasing demands of reliability, safety for the modern complex systems, observer design for system with unknown inputs, which is known as unknown input observer (UIO), has received much more attention in the past decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. However, we find that in the field of UIO, much more attention is put on linear systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Unknown input observer design for Takagi-Sugeno fuzzy stochastic system

Guo

Zhu

2015

Int. J. Control Autom. Syst.

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This paper deals with the unknown input observer design for Takagi-Sugeno (T-S) fuzzy models subjected to measurement noise and stochastic noise. The method applies the singular system theory by considering the measurement noise as an augmented system state, and then an unknown input observer based on the techniques of singular systems is developed to estimate both the system states and measurement noise simultaneously. Under a necessary assumption, the error dynamic system of the observer is free from the unknown inputs. And the observer gain matrix is determined by means of minimum covariance matrix of state residual. Two simulation examples are given to illustrate the correctness and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

“…However, we find that in the field of UIO, much more attention is put on linear systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Only a few results can be found for some specific nonlinear systems, such as for Lipschitz or one-sided Lipschitz nonlinear systems [15,16].…”

Section: Introductionmentioning

confidence: 99%

Unknown input observer design for Takagi-Sugeno fuzzy stochastic system

Guo

Zhu

2015

Int. J. Control Autom. Syst.

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“…This is due to the wide range of applications that already exist for this theory, like fault detection and observer-based control of electromechanical systems that are subjected to measurement noise, uncertainties, and disturbances [5][6][7][8]. In this line, the objective could be disturbance-decoupled observer design [9,10,8], and/or the estimation of the disturbance signals [7,8,11].…”

Section: Introductionmentioning

confidence: 99%

Minimal unknown-input functional observers for multi-input multi-output LTI systems

Mohajerpoor

Asayesh

Nahavandi

2015

Journal of Process Control

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“…This constraint, however, is too strict to be satisfied in practical applications. In order to loosen the observer matching condition, algebraically decomposed technique, 8 unscented transformation (UT), 9 population-based approaches, 10 optimization methods 11,12 and neural networks, 13 have been incorporated into the framework of UIO, which greatly improves the performance of the original method in applications. Stochastic filters 11,14,15 such as Kalman filter and particle filter, have been introduced to solve the fault detection and isolation problems in recent years.…”

Section: Introductionmentioning

confidence: 99%

A data driven approach for detection and isolation of anomalies in a group of UAVs

Wang

2015

Chinese Journal of Aeronautics

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The use of groups of unmanned aerial vehicles (UAVs) has greatly expanded UAV's capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar (dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters. Simulation results have demonstrated the efficiency and flexibility of the proposed approach. ª 2015 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

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Designing fault detection observers for linear systems with mismatched unknown inputs

Cited by 33 publications

References 23 publications

Unknown input observer design for Takagi-Sugeno fuzzy stochastic system

Unknown input observer design for Takagi-Sugeno fuzzy stochastic system

Minimal unknown-input functional observers for multi-input multi-output LTI systems

A data driven approach for detection and isolation of anomalies in a group of UAVs

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