We use a few cookies

Essential cookies keep the library working. With your permission, we'd also like to use analytics to see what's useful and improve the experience. Read our Privacy Policy or Cookie Policy.

Skip to main content
Home/e-Resources/Electrical Engineering/Passive gain-scheduling filtering for jumping linear parameter varying systems with fading channels based on the hidden Markov model
PeriodicalsElectrical Engineering

Passive gain-scheduling filtering for jumping linear parameter varying systems with fading channels based on the hidden Markov model

Checking your access to this record.

Abstract & Overview

This work is aimed to address the passive gain-scheduling filtering problem for Markov jump linear parameter varying systems with fading channels. A hidden Markov process is employed to describe mode information exchange between the system and the presented filter. By the aid of stochastic analysis theory, some sufficient conditions for guaranteeing the existence of an available passive gain-scheduling filter are established. On the basis of the designed filter, the passivity of the filtering error system is ensured in the presence of randomly occurring fading channels. Finally, an explained example is put forward to verify the effectiveness of the filter.

Cite this record

Liang Chi Shen, Xiaofei Yang, Jing Wang, Jianwei Xia (2018). Passive gain-scheduling filtering for jumping linear parameter varying systems with fading channels based on the hidden Markov model. Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, 233(1), 67–79. https://doi.org/10.1177/0959651818777679

Copy in another style

Shen, L. C., Yang, X., Wang, J., & Xia, J. (2018). Passive gain-scheduling filtering for jumping linear parameter varying systems with fading channels based on the hidden Markov model. Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, 233(1), 67–79. https://doi.org/10.1177/0959651818777679
Author names are used as deposited, so check their order before you submit.

Metadata is harvested from open-access indexes and can be incomplete. Check a citation against the published version before relying on it — see our open access & attribution policy.