Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation

Sarthak Chatterjee, Andrea Alessandretti, A. Pedro Aguiar, S.D. Gonçalves Melo Pequito

Research output: Contribution to journalArticleScientificpeer-review

6 Downloads (Pure)


Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies among the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this article addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks, where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks. Our results may significantly contribute to the development of novel neurotechnologies, particularly in the development of state estimation paradigms for neural signals such as EEG, which are often noisy signals known to be affected by artifacts not having any particular stochastic characterization.

Original languageEnglish
Pages (from-to)226-237
JournalIEEE Transactions on Control of Network Systems
Issue number1
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Additives
  • Biological networks
  • cyber-physical systems
  • decision/estimation theory
  • Electroencephalography
  • Linear programming
  • Network systems
  • other applications
  • State estimation
  • Symmetric matrices
  • Uncertainty

Cite this