Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

Xiaoxu Liu, Zhiwei Gao, Richard Binns, Hui Shao
2016 IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society  
Nowadays, industrial plants are calling for highperformance 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
more » ... sidered 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. Keywords-robust fault estimation; stochastic Takagi-Sugeno systems; unknown input observer; stochastic input-to-state stability I.
doi:10.1109/iecon.2016.7793639 dblp:conf/iecon/LiuGBS16 fatcat:65aqtupifvfovhgavgqfjcunoe