Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

Liu, Xiaoxu, Gao, Zhiwei, Binns, Richard and Shao, Hui (2016) Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 24th - 27th October 2016, Firenze, Italy.

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Nowadays, industrial plants are calling for high-performance fault diagnosis techniques to meet stringent requirements on system availability and safety in the event of component failures. This paper deals with robust fault estimation problems for stochastic nonlinear systems subject to faults and unknown inputs relying on Takagi-Sugeno fuzzy models. Augmented approach jointly with unknown input observers for stochastic Takagi-Sugeno models is exploited here, which allows one to estimate both considered faults and full system states robustly. The considered unknown inputs can be either completely decoupled or partially decoupled by observers. For the un-decoupled part of unknown inputs, which still influence error dynamics, stochastic input-to-state stability properties are applied to take nonzero inputs into account and sufficient conditions are achieved to guarantee bounded estimation errors under bounded unknown inputs. Linear matrix inequalities are employed to compute gain matrices of the observer, leading to stochastic input-to-state-stable error dynamics and optimization of the estimation performances against un-decoupled unknown inputs. Finally, simulation on wind turbine benchmark model is applied to validate the performances of the suggested fault reconstruction methodologies.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: stochastic input-to-state stability, robust fault estimation, stochastic Takagi-Sugeno systems, unknown input observer
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Dr Zhiwei Gao
Date Deposited: 06 Jan 2017 09:45
Last Modified: 01 Aug 2021 07:16

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