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

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

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.

Suggested Citation

  • Sakthivel, Ramalingam & Sakthivel, Rathinasamy & Kwon, Oh-Min & Selvaraj, Palanisamy, 2021. "Disturbance rejection for singular semi-Markov jump neural networks with input saturation," Applied Mathematics and Computation, Elsevier, vol. 407(C).
    • Handle: RePEc:eee:apmaco:v:407:y:2021:i:c:s0096300321003908
    DOI: 10.1016/j.amc.2021.126301
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    References listed on IDEAS

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