Hybrid deep learning controller for nonlinear systems based on adaptive learning rates

DOI10.1080/00207179.2022.2067080zbMath1520.93213OpenAlexW4224101147MaRDI QIDQ6134177

Ahmad M. El-Nagar, Unnamed Author, Mohammad El-Bardini, Unnamed Author

Publication date: 25 July 2023

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179.2022.2067080

zbMATH Keywords

nonlinear systemsLyapunov stabilityHebbian learning rulehybrid deep learningKohonen procedureself-organised map

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05)

Cites Work

• Adaptive T-S fuzzy controller using reinforcement learning based on Lyapunov stability
• Mathematical formulations of Hebbian learning
• Closed-loop control of nonlinear neural networks: the estimate of control time and energy cost
• Improving the performance of a class of adaptive fuzzy controller based on stable and fast on-line learning algorithm
• Concepts of Soft Computing
• Adaptive neural network output feedback control for flexible multi-link robotic manipulators
• Neural network-based nonlinear sliding-mode control for an AUV without velocity measurements

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