Fault diagnosis observer for descriptor Takagi-Sugeno systems

López‐Estrada, Francisco‐Ronay; Theilliol, Didier; Astorga‐Zaragoza, C.M.; Ponsart, Jean-Christophe; Valencia‐Palomo, Guillermo; Camas-Anzueto, J.L.

doi:10.1016/j.neucom.2018.11.055

Cited by 31 publications

(19 citation statements)

References 27 publications

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“…If physically redundant sensors are not installed, analytical redundancy approaches are applied that estimate sensor error from the relation between a range of different target sensors. These approaches can be classified into model-based [9,10], knowledge-based [11][12][13], and data-driven methods [14][15][16][17] depending on the understanding of the depth of the system and the available amount of data. Jiang et al (2011) demonstrated this classification of general sensor fault detection [18].…”

Section: Sensor Fault Detection and Identificationmentioning

confidence: 99%

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Choi

Lee

2020

Sensors

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A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states.

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Section: Sensor Fault Detection and Identificationmentioning

confidence: 99%

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Choi

Lee

2020

Sensors

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“…Model-based safety schemes require differential equations representing the complex dynamics presented in physical systems, which are often nonlinear [6,7]. Recently, multimodel techniques such as Linear Parameter Varying (LPV), quasi-LPV (qLPV), and Takagi-Sugeno (TS) systems have emerged as an attractive alternative to deal with the analysis of complex nonlinear systems due to the fact that it is possible to extend techniques developed for linear systems but applied to nonlinear systems [8][9][10][11]. In this paper, it is considered that qLPV and TS systems are the same because the convex model is obtained through the so-called nonlinear sector approach [12].…”

Section: Introductionmentioning

confidence: 99%

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

López‐Estrada

Santos-Estudillo

Valencia‐Palomo

et al. 2020

MCA

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The main aim of this paper is to propose a robust fault-tolerant control for a three degree of freedom (DOF) mechanical crane by using a convex quasi-Linear Parameter Varying (qLPV) approach for modeling the crane and a passive fault-tolerant scheme. The control objective is to minimize the load oscillations while the desired path is tracked. The convex qLPV model is obtained by considering the nonlinear sector approach, which can represent exactly the nonlinear system under the bounded nonlinear terms. To improve the system safety, tolerance to partial actuator faults is considered. Performance requirements of the tracking control system are specified in an H∞ criteria that guarantees robustness against measurement noise, and partial faults. As a result, a set of Linear Matrix Inequalities is derived to compute the controller gains. Numerical experiments on a realistic 3 DOF crane model confirm the applicability of the control scheme.

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“…The TS approach was adopted rapidly by the control community and was applied to state estimation [7], control [8], fault detection [7], descriptor systems [9], state observers [10], waste-water treatment plants [11], bioreactors [12], process industry [13,14], mechatronics [15,16], aeronautics [17,18] and automotive [19,20], among others. Comprehensive material about the topic can be found in Refs.…”

Section: Introductionmentioning

confidence: 99%

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

2019

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This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).

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Fault diagnosis observer for descriptor Takagi-Sugeno systems

Cited by 31 publications

References 27 publications

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

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