Intelligent state estimation for fault tolerant nonlinear predictive control

Deshpande, Anjali; Patwardhan, Sachin C.; Narasimhan, Shankar

doi:10.1016/j.jprocont.2008.04.006

Cited by 74 publications

(37 citation statements)

References 23 publications

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“…(10) and (11) should be initialized with J y (t ; t) = [0] n×r and G y (t ; t) = [0] r×r . This is due to the fact that at time instant t the fault magnitude is zero.…”

Section: Lemmamentioning

confidence: 99%

“…In the GLR approach proposed by Prakash et al [10,14] it is suggested to compute the FCT test for data window [t 1 , t 1 + N] which is obtained after rejection of the FDT test at time instant t 1 . Upon rejection of FCT test, it is assumed that t 1 is the TOF and subsequently the likelihood ratio is computed for all hypothesized faults in the interval [t 1 , t 1 + N] to isolate and estimate the fault magnitude.…”

Section: Finding the Candidate Data Window For Occurrence Of The Faultmentioning

confidence: 99%

“…The ultimate goal of such methods is to accurately detect the time of occurrence of the fault, identify its location and estimate its magnitude. More recently, the concept of fault tolerant control and integration of the fault detection and isolation (FDI) module with controller design has been widely studied in the literature [7][8][9][10][11]. The general approach in active fault tolerant control systems is to modify the controller according to the supervisory information provided by the FDI module in order to mitigate the impact of the fault on the system performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection and diagnosis of incipient faults in sensors of an LTI system using a modified GLR-based approach

Kiasi

Prakash

Shah

2015

Journal of Process Control

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“…(10) and (11) should be initialized with J y (t ; t) = [0] n×r and G y (t ; t) = [0] r×r . This is due to the fact that at time instant t the fault magnitude is zero.…”

Section: Lemmamentioning

confidence: 99%

Section: Finding the Candidate Data Window For Occurrence Of The Faultmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection and diagnosis of incipient faults in sensors of an LTI system using a modified GLR-based approach

Kiasi

Prakash

Shah

2015

Journal of Process Control

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“…The results are presented in the next subsection. For the simulations the motor parameters provided We referred to [27] for the comparative analysis of both the algorithms. Fig.…”

Section: B Matlabisimullnk Model For a Ekf Based Sensorless Vector Cmentioning

confidence: 99%

Study of sensorless control algorithms for a permanent magnet synchronous motor vector control drive

Walambe

Joshi

Apte³

et al. 2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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In the recent past, the use of permanent magnet synchronous motors (PMSM) has increased considerably owing to their inherent advantages. The high performance speed and/or position control of a PMS motor requires an accurate knowledge of rotor shaft position and angular speed in order to synchronize the phase excitation pulses to the rotor position. The rotor position and speed information is specifically required for implementation of vector control scheme in a PMSM. In order to achieve the speed control various sensored methods are employed. Due to number of advantages recent trend is to implement the sensorless control drive for a PMSM. This paper presents and compares two main sensorless control algorithms viz, Sliding Mode observer(SMO) and Extended Kalman Filter (EKF) for sensorless estimation of rotor position and speed in a PMSM. The simulation results for both these methods on an identical PMSM are presented.The analysis of these results give some insights into the accuracy of estimations and subsequent working of the PMSM with them.

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“…For example, a nonlinear GLR version worked with the EKF was proposed in [8] to deal with the radar tracking problems. In [9], a fault tolerant nonlinear predictive control scheme is proposed, in which the nonlinear GLR worked with an EKF, too. Although the EKF-based approach appear perfectly straightforward, the linearized approximation can be extremely poor.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear version of the generalized likelihood ratio test

Liu

Zhong

2014

Proceedings of the 33rd Chinese Control Conference

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In this paper, a new version of the generalized likelihood ratio (GLR) test is proposed to deal with the fault diagnosis problem in nonlinear systems. The unscented Kalman filter (UKF) is utilized for state estimation and innovation generation. The fault signatures used in the hypotheses test are pre-computed by running the fault model and the nominal model simultaneously based on the state estimations. In such a way the intolerable computational problem, which arise from applying a bank of nonlinear filters, is solved. Further more, the fault isolation problem as well as the fault estimation problem of the non-abrupt fault is discussed based on the nonlinear GLR approach. The simulation study of a longitudinal aircraft model is utilized to show the effectiveness of the proposed method.

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Intelligent state estimation for fault tolerant nonlinear predictive control

Cited by 74 publications

References 23 publications

Detection and diagnosis of incipient faults in sensors of an LTI system using a modified GLR-based approach

Detection and diagnosis of incipient faults in sensors of an LTI system using a modified GLR-based approach

Study of sensorless control algorithms for a permanent magnet synchronous motor vector control drive

A nonlinear version of the generalized likelihood ratio test

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