State And Fault Estimation For Discrete-Time Stochastic Systems With Event-Triggered Mechanism In Networked Environments

In this thesis,under the networked environments,the issues of the event-triggered state and fault estimation are investigated for several kinds of discrete stochastic systems which are widely concerned in practical engineering systems.The networked phenomena considered mainly include censored measurements,sensor saturations,sensor degradations and randomly occurring nonlinearities.In order to eliminate the negative effects caused by the limited bandwidth of communication channel as well as the limited energy carried by sensors,the event-triggered mechanism is introduced to improve the utilization of resources.On the other hand,the widely applied stochastic systems in theoretical analysis and engineering practice are taken into consideration,which are uncertain systems,multi-rate systems,state-saturated systems and complex networks.The content of this paper is mainly divided into three parts.In the first part,the issues of recursive filtering are studied for discrete stochastic systems under the eventtriggered mechanism.The recursive filters are designed where the effects of censored measurements,saturations and randomly occurring nonlinearities are considered,and the corresponding performance indices are satisfied.In the second part,the eventtriggered fusion estimation problem is investigated for a class of multi-rate systems subject to sensor degradations where the new design approaches of local recursive filters and fusion algorithm are proposed.The third part is concerned with the state and fault estimation problem for a class of nonlinear stochastic systems where the effects of the sensor saturations and the event-triggered mechanism are taken into account simultaneously,and a new design approach of state and fault estimator is proposed.In a word,the compendious frame and descriptions of the thesis are given as follows.· The recursive filtering problem is investigated for time-delayed state-saturated systems with parameter uncertainties.The considered parameter uncertainties are described by norm bounded.For the sake of saving the limited communication resources,we utilize the event-triggered mechanism to govern the frequency of the transmissions.Based on the received measurements,a recursive filter is constructed,such that an upper bound of the filtering error covariance is guar-anteed where all parameter uncertainties,state saturation and event-triggered mechanism are taken into account.The desired filter gain is parameterized by means of minimizing the obtained upper bound.· The issue of the dynamic event-triggered recursive filtering is considered for uncertain systems subject to censored measurements.The censored measurement under consideration is characterized by the celebrated Tobit measurement model,and the parameter uncertainties are assumed to be norm bounded.For further reducing the limited communication resources,we introduce the dynamic event-triggered mechanism.By means of mathematical induction,the upper bound of the filtering error covariance is derived,and then the filter parameter is designed in the sense of minimizing the derived upper bound.· The resilient filtering problem is studied for nonlinear complex networks subject to state saturation and randomly occurring nonlinearities under the eventtriggered mechanism.A set of Bernoulli-distributed variables with known probabilities are introduced to model randomly occurring nonlinearities,and a signum function is used to describe the state saturation.Based on the established measurement model,an event-triggered recursive filter is constructed.Then,the filter parameter is designed to minimize the upper bound of the filtering error covariance.· The fusion estimation problem is addressed for multi-rate systems subject to sensor degradations under the event-triggered mechanism.A set of random variables obeying known probabilities are used to characterize the phenomena of the sensor degradations.To facilitate the filter design,the multi-rate system is transformed into a single-rate system by using an augmentation approach.Based on the received measurements of each sensor node,the corresponding local recursive filter is constructed where the upper bound of each local filtering error covariance is guaranteed.With the aid of completing-square technique,we obtain the filter gain which is capable of minimizing the upper bound.Finally,we derive the fusion estimation by virtue of the improved covariance intersection fusion approach.· The event-triggered state and fault estimation problem is investigated for a class of nonlinear systems subject to sensor saturations and fault signals.The signum function is used to describe the sensor saturation.In order to save the limited communication resources,an event-triggered mechanism is introduced to regulate the transmission frequency from the sensor to the estimator.For the purpose of estimating the state and fault simultaneously,based on the received measurement,an event-triggered recursive state and fault estimator is constructed such that the estimation error covariances for the state and fault are both guaranteed with upper bounds and subsequently derive the gain matrices minimizing such upper bounds,relying on the solutions to a set of difference equations.