Event-based state and fault estimation for nonlinear systems with logarithmic quantization and missing measurements

2019 ◽  
Vol 356 (7) ◽  
pp. 4076-4096 ◽  
Author(s):  
Shaoying Wang ◽  
Xuegang Tian ◽  
Huajing Fang
Keyword(s):  
Nonlinear Systems ◽  
Fault Estimation ◽  
Missing Measurements ◽  
Event Based

scholarly journals Recursive State and Random Fault Estimation for Linear Discrete Systems under Dynamic Event-Based Mechanism and Missing Measurements

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xuegang Tian ◽  
Shaoying Wang
Keyword(s):  
Upper Bound ◽  
Estimation Error ◽  
Discrete Systems ◽  
Fault Estimation ◽  
Missing Measurements ◽  
Bernoulli Random Variables ◽  
Dynamic Event ◽  
Error Covariance ◽  
Event Based ◽  
Linear Discrete Systems

This paper is concerned with the event-based state and fault estimation problem for a class of linear discrete systems with randomly occurring faults (ROFs) and missing measurements. Different from the static event-based transmission mechanism (SETM) with a constant threshold, a dynamic event-based mechanism (DETM) is exploited here to regulate the threshold parameter, thus further reducing the amount of data transmission. Some mutually independent Bernoulli random variables are used to characterize the phenomena of ROFs and missing measurements. In order to simultaneously estimate the system state and the fault signals, the main attention of this paper is paid to the design of recursive filter; for example, for all DETM, ROFs, and missing measurements, an upper bound for the estimation error covariance is ensured and the relevant filter gain matrix is designed by minimizing the obtained upper bound. Moreover, the rigorous mathematical analysis is carried out for the exponential boundedness of the estimation error. It is clear that the developed algorithms are dependent on the threshold parameters and the upper bound together with the probabilities of missing measurements and ROFs. Finally, a numerical example is provided to indicate the effectiveness of the presented estimation schemes.

Event-based recursive filtering for time-delayed stochastic nonlinear systems with missing measurements

2017 ◽  
Vol 134 ◽  
pp. 158-165 ◽  
Author(s):  
Jingyang Mao ◽  
Derui Ding ◽  
Yan Song ◽  
Yurong Liu ◽  
Fuad E. Alsaadi
Keyword(s):  
Nonlinear Systems ◽  
Stochastic Nonlinear Systems ◽  
Missing Measurements ◽  
Recursive Filtering ◽  
Event Based

scholarly journals Event-Triggering State and Fault Estimation for a Class of Nonlinear Systems Subject to Sensor Saturations

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1242
Author(s):  
Cong Huang ◽  
Bo Shen ◽  
Lei Zou ◽  
Yuxuan Shen
Keyword(s):  
Nonlinear Systems ◽  
Difference Equations ◽  
Estimation Error ◽  
Upper Bounds ◽  
The State ◽  
Fault Estimation ◽  
Triggering Mechanism ◽  
Transmission Frequency ◽  
Current Transmission ◽  
Event Triggering

This paper is concerned with the state and fault estimation issue for nonlinear systems with sensor saturations and fault signals. For the sake of avoiding the communication burden, an event-triggering protocol is utilized to govern the transmission frequency of the measurements from the sensor to its corresponding recursive estimator. Under the event-triggering mechanism (ETM), the current transmission is released only when the relative error of measurements is bigger than a prescribed threshold. The objective of this paper is to design an event-triggering recursive state and fault estimator 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. Finally, two experimental examples are given to validate the effectiveness of the designed algorithm.

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