Print Email Facebook Twitter Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation Title Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation Author Chatterjee, Sarthak (Rensselaer Polytechnic Institute) Alessandretti, Andrea (Magneti Marelli) Aguiar, A. Pedro (Universidade do Porto) Gonçalves Melo Pequito, S.D. (TU Delft Team Sergio Pequito) Date 2023 Abstract 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. Subject AdditivesBiological networkscyber-physical systemsdecision/estimation theoryElectroencephalographyLinear programmingNetwork systemsother applicationsState estimationSymmetric matricesUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:9491e6e1-fbce-4369-9049-cc3fd9202827 DOI https://doi.org/10.1109/TCNS.2022.3198832 Embargo date 2023-02-16 ISSN 2325-5870 Source IEEE Transactions on Control of Network Systems, 10 (1), 226-237 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. Part of collection Institutional Repository Document type journal article Rights © 2023 Sarthak Chatterjee, Andrea Alessandretti, A. Pedro Aguiar, S.D. Gonçalves Melo Pequito Files PDF Discrete_Time_Fractional_ ... mation.pdf 1.28 MB Close viewer /islandora/object/uuid:9491e6e1-fbce-4369-9049-cc3fd9202827/datastream/OBJ/view