Disturbance rejection for singular semi-Markov jump neural networks with input saturation

Ramalingam Sakthivel, Rathinasamy Sakthivel, Oh-Min Kwon and Palanisamy Selvaraj

Applied Mathematics and Computation, 2021, vol. 407, issue C

Abstract: In this paper, robust disturbance rejection based stabilization problem for singular semi-Markov jump neural networks in the presence of random gain fluctuation and input saturation is investigated by using improved equivalent-input-disturbance (IEID) estimator method. In particular, the estimator integrates a gain factor to improve the flexibility of the system performance and to enhance the dynamic efficiency of disturbance rejection. In addition, the randomness phenomena are integrated by the stochastic variables agency which satisfies the properties of the Bernoulli distribution. Precisely, to alleviate the disturbance effect in the closed-loop system, the IEID-based estimator is added into the feedback control input without any prior information about the disturbance effect. With the aid of stochastic analysis and Wirtinger’s inequality techniques, the desired IEID-based non-fragile feedback controller can be obtained by solving a set of linear matrix inequalities. At last, two illustrative simulation results are presented to validate the effectiveness of the developed theoretical developments.

Keywords: Singular neural networks; Non-fragile control; Improved equivalent-input-disturbance; Semi-Markov jump; Input saturation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300321003908
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:407:y:2021:i:c:s0096300321003908

DOI: 10.1016/j.amc.2021.126301

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().