Fault Estimation And Application Of Stochastic Systems With Incomplete Measurements

With the development of network communication technology,modern industry has also embarked on the road of network development.The network industrial system has become the development trend of modern industry.The so-called network control system is a spatial distributed control system composed of closed-loop loops composed of networks.The system connects the components distributed in different positions,such as sensors,controllers and actuators,in one network From now on,compared with traditional industrial system,networked system has many advantages,such as low installation cost,simple wiring,convenient maintenance and so on.But at the same time,the networked system also has the problems of delay,out of order,measurement loss and so on.These problems bring new challenges to the fault estimation and fault-tolerant control of networked control systems.Although there are many research achievements in this field in recent years,there are still many problems worthy of further exploration.The main work and innovation of this paper are as follows:(1)Event-based robust state and fault estimation for stochastic linear system with missing observations and uncertaintyThis paper investigates event-based robust state and fault estimation problem for stochastic linear system with missing observations and norm-bounded uncertainty.Missing observations is depicted by Bernoulli distributed process.The measurements transmit to estimator unless the current innovation disobey the Send-on-Delta(SoD)conditions.Filtering algorithm is constructed by virtue of augmented state method combined with two-stage robust event-based estimators.Upper bounds of the estimation error covariance are obtained respectively according to solving discrete Riccati difference equations.Subsequently,by appropriately formulating the filter gain matrices such that the trace of the upper bounds are minimal.Simulation results demonstrate the usefulness of the algorithm(2)Event-based state and fault estimation for stochastic nonlinear system with Markov packet dropoutThis paper investigates the event-based state and fault estimation problem for stochastic nonlinear system with Markov packet dropout.By introducing the fictitious noise,the fault is augmented to the system state.Then combining the unscented Kalman filter(UKF)with event-triggered and Markov packet dropout.the modified UKF is proposed to estimate the state and fault.The filter error covariance is proved to be bounded by selecting suitable event-triggered threshold.Meanwhile,sufficient conditions are obtained to guarantee the stochastic stability of the proposed filter.Finally,simulation result illustrates the performance of our main method.(3)Fault estimation and fault tolerant control for linear stochastic uncertain systemsFault estimation and fault tolerant control(FTC)problems are addressed for linear stochastic uncertain systems.Owing to the existing of uncertain term,two-stage robust estimators are applied for state estimation,which guarantee the upper bounds of the estimation error covariance exist and the trace of the estimation error covariance are minimized.The fault is assumed to be incipient fault,then state and fault estimation can be derived simultaneously by augmenting the fault as a part of state.According to the fault estimation,the FTC is presented to secure the system is stable for the control loop and the state of the system with fault converges asymptotically to the state of the system without fault.Finally,simulation result demonstrates the effectiveness of our main method.