Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model

Dezhi Xu; Bin Jiang; Peng Shi

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 1, page 183-196
  • ISSN: 1641-876X

Abstract

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Based on a Takagi-Sugeno (T-S) fuzzy model and an inverse system method, this paper deals with the problem of actuator fault estimation for a class of nonlinear dynamic systems. Two different estimation strategies are developed. Firstly, T-S fuzzy models are used to describe nonlinear dynamic systems with an actuator fault. Then, a robust sliding mode observer is designed based on a T-S fuzzy model, and an inverse system method is used to estimate the actuator fault. Next, the second fault estimation strategy is developed. Compared with some existing techniques, such as adaptive and sliding mode methods, the one presented in this paper is easier to be implemented in practice. Finally, two numerical examples are given to demonstrate the efficiency of the proposed techniques.

How to cite

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Dezhi Xu, Bin Jiang, and Peng Shi. "Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model." International Journal of Applied Mathematics and Computer Science 22.1 (2012): 183-196. <http://eudml.org/doc/208094>.

@article{DezhiXu2012,
abstract = {Based on a Takagi-Sugeno (T-S) fuzzy model and an inverse system method, this paper deals with the problem of actuator fault estimation for a class of nonlinear dynamic systems. Two different estimation strategies are developed. Firstly, T-S fuzzy models are used to describe nonlinear dynamic systems with an actuator fault. Then, a robust sliding mode observer is designed based on a T-S fuzzy model, and an inverse system method is used to estimate the actuator fault. Next, the second fault estimation strategy is developed. Compared with some existing techniques, such as adaptive and sliding mode methods, the one presented in this paper is easier to be implemented in practice. Finally, two numerical examples are given to demonstrate the efficiency of the proposed techniques.},
author = {Dezhi Xu, Bin Jiang, Peng Shi},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {actuator fault estimation; Takagi-Sugeno fuzzy models; robust sliding mode observer; inverse system method},
language = {eng},
number = {1},
pages = {183-196},
title = {Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model},
url = {http://eudml.org/doc/208094},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Dezhi Xu
AU - Bin Jiang
AU - Peng Shi
TI - Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 1
SP - 183
EP - 196
AB - Based on a Takagi-Sugeno (T-S) fuzzy model and an inverse system method, this paper deals with the problem of actuator fault estimation for a class of nonlinear dynamic systems. Two different estimation strategies are developed. Firstly, T-S fuzzy models are used to describe nonlinear dynamic systems with an actuator fault. Then, a robust sliding mode observer is designed based on a T-S fuzzy model, and an inverse system method is used to estimate the actuator fault. Next, the second fault estimation strategy is developed. Compared with some existing techniques, such as adaptive and sliding mode methods, the one presented in this paper is easier to be implemented in practice. Finally, two numerical examples are given to demonstrate the efficiency of the proposed techniques.
LA - eng
KW - actuator fault estimation; Takagi-Sugeno fuzzy models; robust sliding mode observer; inverse system method
UR - http://eudml.org/doc/208094
ER -

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Citations in EuDML Documents

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  1. Ali Ben Brahim, Slim Dhahri, Fayçal Ben Hmida, Anis Sellami, An H sliding mode observer for Takagi-Sugeno nonlinear systems with simultaneous actuator and sensor faults
  2. Rodolfo Orjuela, Benoît Marx, José Ragot, Didier Maquin, Nonlinear system identification using heterogeneous multiple models
  3. Andreas Rauh, Saif S. Butt, Harald Aschemann, Nonlinear state observers and extended Kalman filters for battery systems
  4. Silvio Simani, Residual generator fuzzy identification for automotive diesel engine fault diagnosis
  5. Hoda Moodi, Mohammad Farrokhi, Robust observer design for Sugeno systems with incremental quadratic nonlinearity in the consequent
  6. Abdel-Razzak Merheb, Hassan Noura, François Bateman, Design of passive fault-tolerant controllers of a quadrotor based on sliding mode theory

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