Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31527
Title: Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms
Authors: Zhang, Y
Wang, Z
Zou, L
Qian, W
Du, S
Keywords: networked nonlinear systems;neural networks;unknown nonlinearities;recursive state estimation;binary-encoding mechanism
Issue Date: 21-Mar-2025
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Zhang, Y. et al. (2025) 'Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms', IEEE Transactions on Neural Networks and Learning Systems, 36 (5), pp. 10072 - 10083. doi: 10.1109/TNNLS.2025.3542492.
Abstract: This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.
URI: https://bura.brunel.ac.uk/handle/2438/31527
DOI: https://doi.org/10.1109/TNNLS.2025.3542492
ISSN: 2162-237X
Other Identifiers: ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401
ORCiD: Lei Zou https://orcid.org/0000-0002-0409-7941
ORCiD: Wei Qian https://orcid.org/0000-0002-3994-6501
ORCiD: Shuxin Du https://orcid.org/0000-0002-8530-4884
Appears in Collections:Dept of Computer Science Research Papers

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