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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 15/2021

16.01.2021 | Original Article

Full-state neural network observer-based hybrid quantum diagonal recurrent neural network adaptive tracking control

verfasst von: Ahmed Elkenawy, Ahmad M. El-Nagar, Mohammad El-Bardini, Nabila M. El-Rabaie

Erschienen in: Neural Computing and Applications | Ausgabe 15/2021

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Abstract

This study introduces a neural network (NN) adaptive tracking controller-based reinforcement learning (RL) scheme for unknown nonlinear systems. First, an observer using feed-forward NN (FFNN) is performed to estimate the controlled system states. Second, an adaptive control based on actor-critic RL is developed, in which a quantum diagonal recurrent neural network (QDRNN) is proposed to represent the critic and actor parts. The critic QDRNN is applied to perform the “strategic” utility function, and it is minimized by the actor QDRNN. The proposed adaptive tracking NN control guarantees the faster convergence due to the developed updated algorithm for the controller parameters, which is derived using the Lyapunov function. Simulation and practical results indicate the robustness of the proposed observer-based adaptive control relative to other existing controllers.
Vorheriger Artikel A picture fuzzy similarity measure based on direct operations and novel multi-attribute decision-making method
Nächster Artikel ID-Net: an improved mask R-CNN model for intrusion detection under power grid surveillance

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Metadaten
Titel
Full-state neural network observer-based hybrid quantum diagonal recurrent neural network adaptive tracking control
verfasst von
Ahmed Elkenawy
Ahmad M. El-Nagar
Mohammad El-Bardini
Nabila M. El-Rabaie
Publikationsdatum
16.01.2021
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 15/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05685-x

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