Robust fault estimation for stochastic nonlinear systems with Brownian perturbations

Liu, Xiaoxu; Odofin, Sarah

doi:10.1109/iciea.2016.7603624

Cited by 1 publication

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Definition 3 [24]. A system is said to be stochastic input-tostate stable, if , there exist functions , , such that for continuous bounded input and any initial condition , we have for , (4) As mentioned in our previous work [12], globally asymptotically stability in probability defined in [25] is a specific situation of stochastic input-to-state stability when input .…”

Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

“…In our previous work [12], sufficient proof has been provided of the following lemma. Lemma [12]: A system is stochastic input-to-state stable if there exist a stochastic input-to-state stability-Lyapunov function.…”

Section: Robust Fault Estimation Schemementioning

confidence: 99%

“…Lemma [12]: A system is stochastic input-to-state stable if there exist a stochastic input-to-state stability-Lyapunov function.…”

Section: Robust Fault Estimation Schemementioning

confidence: 99%

“…Our early work [11], represents the first attempt to introduce unknown input observer-based fault estimation into linear systems and Lipschitz nonlinear systems subject to partially decoupled unknown inputs, associated with linear matrix inequalities to optimize the estimation performance. The work has been extended to stochastic nonlinear systems satisfying quadratic innerbounded conditions in [12]. However, it is worth noting that for general forms of stochastic nonlinear systems, this issue meet challenges due to lack of systematic way to settle observer gains for such complex systems, thus remains to be an area to be explored.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust fault estimation for stochastic Takagi-Sugeno fuzzy systems

Liu

Binns

Shao

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

Self Cite

View full text Add to dashboard Cite

Abstract-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 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.

show abstract

Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

Section: Robust Fault Estimation Schemementioning

confidence: 99%

“…Lemma [12]: A system is stochastic input-to-state stable if there exist a stochastic input-to-state stability-Lyapunov function.…”

Section: Robust Fault Estimation Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations