Resilient 
                
                  
                
                $$H_{\infty }$$
                
                  
                    
                      H
                      ∞
                    
                  
                
               Filtering for Stochastic Systems with Randomly Occurring Gain Variations, Nonlinearities and Channel Fadings

In this article, the problem of resilient finite-time fault detection dissipative-based filter design is investigated for a class discrete-time semi-Markovian jump systems with incomplete measurements, dynamic quantization and time-varying delay. The considered system includes various network-induced imperfections such as missing measurements, transmission delay and dynamic signal quantization. Specifically, all these imperfections are modeled using stochastic variables following Bernoulli random distribution. In addition, the transition probability of the semi-Markov process is time-varying along with the lower and upper bounds of the transition rate. Eventually, a set of sufficient conditions is derived in terms of linear matrix inequalities (LMIs) by assuming proper Lyapunov-Krasovskii functional and using S-procedure lemma such that the augmented filtering error system is stochastically finite-time bounded with prescribed dissipative performance. Finally, the applicability and usefulness of the proposed filter design is demonstrated through a pulse-width-modulation-boost-converter (PWM) model.

show abstract

Resilient finite‐time fault detection dissipative‐based filter for semi‐Markovian jump systems with incomplete measurements

Sakthivel

Nithya²,

Suveetha

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

Geng

Wang

et al. 2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, the Tobit Kalman filtering problem is investigated for a class of discrete time‐varying fractional‐order systems in the presence of measurement censoring and stochastic nonlinearities under the Round‐Robin protocol (RRP). The fractional‐order dynamic model is described by the Grunwald–Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state‐dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol‐based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off‐line. Simulation results are provided to demonstrate the usefulness of the proposed method.

show abstract

An Improved Approach to Robust $$H_\infty $$ Filtering for Uncertain Discrete-Time Systems with Multiple Delays

Dong

Chen

Qian

2019

Circuits Syst Signal Process

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Resilient $$H_{\infty }$$ H ∞ Filtering for Stochastic Systems with Randomly Occurring Gain Variations, Nonlinearities and Channel Fadings

Cited by 9 publications

References 39 publications

Resilient finite‐time fault detection dissipative‐based filter for semi‐Markovian jump systems with incomplete measurements

Resilient finite‐time fault detection dissipative‐based filter for semi‐Markovian jump systems with incomplete measurements

Tobit Kalman filtering for fractional‐order systems with stochastic nonlinearities under Round‐Robin protocol

An Improved Approach to Robust $$H_\infty $$ Filtering for Uncertain Discrete-Time Systems with Multiple Delays

Contact Info

Product

Resources

About