Neural network-based event-triggered fault detection for nonlinear Markov jump system with frequency specifications

2021 ◽  
Vol 103 (3) ◽  
pp. 2671-2687
Author(s):  
Qi-Dong Liu ◽  
Yue Long ◽  
Ju H. Park ◽  
Tieshan Li
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Markov Jump ◽  
Markov Jump System ◽  
Event Triggered

Event-triggered asynchronous H∞ fault-tolerant control for discrete-time Markov jump system with actuator faults

2020 ◽  
pp. 095965182096573
Author(s):  
Ai-Min Wang ◽  
Jian-Ning Li
Keyword(s):  
Markov Models ◽  
Fault Tolerant ◽  
Actuator Faults ◽  
Closed Loop System ◽  
Network Resources ◽  
Markov Jump ◽  
External Disturbances ◽  
Controlled System ◽  
Markov Jump System ◽  
Event Triggered

This article focuses on the design of event-triggered asynchronous [Formula: see text] fault-tolerant controller for Markov jump system subject to actuator faults and external disturbances. The asynchronization phenomenon not only occurs between the controlled system and controller but also exists between the controlled system and faulty actuator, which are portrayed as two corresponding hidden Markov models. Moreover, a mode-dependent event-triggered mechanism is introduced to facilitate network resources utilization. Then, by introducing mode-dependent Lyapunov-Krasovskii functional, a sufficient condition is obtained to guarantee that the closed-loop system is randomly mean square stable with [Formula: see text] performance. Finally, two numerical examples are employed to illustrate the effectiveness of the proposed synthesis scheme.

Bayesian Sensor Fault Detection in a Markov Jump System

2017 ◽  
Vol 19 (4) ◽  
pp. 1465-1481 ◽  
Author(s):  
Hamed Habibi ◽  
Ian Howard ◽  
Reza Habibi
Keyword(s):  
Fault Detection ◽  
Sensor Fault ◽  
Markov Jump ◽  
Markov Jump System ◽  
Sensor Fault Detection

scholarly journals Finite-time Projective Synchronization for Semi-markovian Neural Networks Systems via Event-triggered Sliding Mode Control

2021 ◽  
Author(s):  
Junchao Ren ◽  
xuejiao Li
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Projective Synchronization ◽  
Linear Matrix ◽  
Markov Jump ◽  
Mode Control ◽  
Markov Jump System ◽  
Event Triggered

Abstract This paper investigates the projection synchronization problem of stochastic neural networked systems based on event-triggered sliding mode control (SMC) covering a finite-time period. For improve transmission efficiency and save network resources, a related event-triggered scheme is proposed for the error system, which can identify whether the measurement error should be transmitted to the controller. For finite-time projective synchronization under given event-triggered mechanism, a semi-Markov jump system model is proposed. Secondly, by creating Lyapunov Krasovsky functional and using linear matrix inequality (LMI) technology, as well as considering a proper sliding surface, a sliding mode controller is designed to implement finite-time projection synchronization of different neural networks. Finally, numerical simulations are exploited to illustrate the effectiveness of the main results.

scholarly journals Dynamic-Event-Based Fault Detection for Markov Jump Systems Under Hidden-Markov Mode Observation

2020 ◽  
Vol 24 (7) ◽  
pp. 917-924
Author(s):  
Xiaoxiao Xu ◽  
Xiongbo Wan ◽  
◽  
◽  
Keyword(s):  
Fault Detection ◽  
Hidden Markov ◽  
Linear Matrix ◽  
Markov Jump ◽  
Markov Jump Systems ◽  
Dynamic Event ◽  
Special Cases ◽  
Jump Systems ◽  
Hidden Markov Mode ◽  
Event Triggered

The fault detection (FD) problem is investigated for event-triggered discrete-time Markov jump systems (MJSs) with hidden-Markov mode observation. A dynamic-event-triggered mechanism, which includes some existing ones as special cases, is proposed to reduce unnecessary data transmissions to save network resources. Mode observation of the MJS by the FD filter (FDF) is governed by a hidden Markov process. By constructing a Markov-mode-dependent Lyapunov function, a sufficient condition in terms of linear matrix inequalities (LMIs) is obtained under which the filtering error system of the FD is stochastically stable with a prescribed H∞ performance index. The parameters of the FDF are explicitly given when these LMIs have feasible solutions. The effectiveness of the FD method is demonstrated by two numerical examples.

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