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Order-dependent sampling control for state estimation of uncertain fractional-order neural networks system
  • +1
  • Qi Zhang,
  • chao ge,
  • Hong Wang,
  • Lei Wang
Qi Zhang
North China University of Science and Technology

Corresponding Author:1526167650@qq.com

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chao ge
North China University of Science and Technology
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Hong Wang
North China University of Science and Technology
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Lei Wang
Tangshan College
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Abstract

In this paper, the problem of state estimation for a fractional-order neural networks system with uncertainties is studied by a sampled-data controller. First, considering the convenience of digital field, such as anti-interference, not affected by noise, a novel sampled-data controller is designed for the fractional-order neural network system of uncertainties with changeable sampling time. In the light of the input delay approach, the sampled-data control system of fractional-order is simulated by the delay system. The main purpose of the presented method is to obtain a sampled-data controller gain K to estimate the state of neurons, which can guarantee the asymptotic stability of the closed-loop fractional-order system. Then, the fractional-order Razumishin theorem and linear matrix inequalities (LMIs) are utilized to derive the stable conditions. Improved delay-dependent and order-dependet stability conditions are given in the form of LMIs. Furthermore, the sampled-data controller can be acquired to promise the stability and stabilization for fractional-order system. Finally, two numerical examples are proposed to demonstrate the effectiveness and advantages of the provided method.
10 Jul 2023Submitted to Optimal Control, Applications and Methods
10 Jul 2023Review(s) Completed, Editorial Evaluation Pending
10 Jul 2023Submission Checks Completed
10 Jul 2023Assigned to Editor
11 Jul 2023Reviewer(s) Assigned
31 Aug 2023Editorial Decision: Revise Minor
08 Sep 20231st Revision Received
09 Sep 2023Submission Checks Completed
09 Sep 2023Assigned to Editor
09 Sep 2023Review(s) Completed, Editorial Evaluation Pending
11 Sep 2023Reviewer(s) Assigned
26 Oct 2023Editorial Decision: Accept