On the approximation capabilities of the homogeneous Takagi-Sugeno model

Fantuzzi, Cesare; Rovatti, Riccardo

doi:10.1109/fuzzy.1996.552326

Cited by 138 publications

(71 citation statements)

References 13 publications

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“…& Xu, W. L. (2000)) and reduces the number of rules in modelling higher order nonlinear systems (Takagi T. & Sugeno, M. (1985)) and (Gang, F. (2006)). T-S fuzzy models are proved to be universal function approximators as they are able to approximate any smooth nonlinear functions to any degree of accuracy in any convex compact region ( Fantuzzi, C. & Rovatti, R. (1996). ), (Johansen, T. A.;Shorten, R. & MurraySmith, R. (2000) ), (Ying, H. (1998)) and (Zeng, K.;Zhang, N. Y.…”

Section: Overview Of Identification and Estimation Of Fuzzy Systemsmentioning

confidence: 99%

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Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Jiménez¹,

Al‐Hadithi²,

Matı́a³

2011

Fuzzy Controllers, Theory and Applications

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Section: Overview Of Identification and Estimation Of Fuzzy Systemsmentioning

confidence: 99%

“…). This fuzzy modelling method presents an alternative technique to represent complex nonlinear systems (Fantuzzi, C. & Rovatti, R. (1996). ), (Ying, H. (1998)) and (Zeng, K.;Zhang, N. Y.…”

Section: Overview Of Identification and Estimation Of Fuzzy Systemsmentioning

confidence: 99%

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Jiménez¹,

Al‐Hadithi²,

Matı́a³

2011

Fuzzy Controllers, Theory and Applications

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“…Moreover, the properties of fuzzy prototypes as universal approximators, and the 428 S. Simani identification capabilities of fuzzy output predictors have been investigated, e.g., by Simani et al (2003), Rovatti (1996), or Fantuzzi and Rovatti (1996).…”

Section: Actuatorsmentioning

confidence: 99%

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Simani¹

2013

International Journal of Applied Mathematics and Computer Science

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Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

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“…in [19,20]. According to the TS modelling approach, the consequents become crisp functions of the input, while the antecedents remain fuzzy propositions, therefore the fuzzy rule takes the form of Eq.…”

Section: Fuzzy System Modellingmentioning

confidence: 99%

Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

Simani¹,

Castaldi²,

Farsoni³

2017

Preprint

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Abstract:The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

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On the approximation capabilities of the homogeneous Takagi-Sugeno model

Cited by 138 publications

References 13 publications

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model

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On the approximation capabilities of the homogeneous Takagi-Sugeno model

Cited by 138 publications

References 13 publications

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Data&ndash;Driven Fault Diagnosis of a Wind Farm Benchmark Model

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Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model