Hybrid Control of the DC Microgrid Using Deep Neural Networks and Global Terminal Sliding Mode Control with the Exponential Reaching Law
MA Sharaf, H Armghan, N Ali, A Yousef, YS Abdalla, AR Boudabbous
Sensors 23 (23), 9342
11
Cites
2023
Year
AI for Power Systems
Large Action Models & AI Integration
Emerging research direction connecting deep learning, soft computing, large-language-model operations, and intelligent decision support for smart-grid control.
The practical question is how AI can act safely inside engineered systems: diagnosing, forecasting, assisting operators, and augmenting nonlinear controllers without replacing the physics.
Selected publications
Google Scholar profile snapshot
Deep learning-based barrier-function super-twisting sliding mode control for integrating renewables in smart grid
H Armghan, Y Xu, Y Xue, N Ali
IET Smart Grid 8 (1), e12201
3
Cites
2025
Year
Fractional-Order Swarming Intelligence Heuristics for Nonlinear Sliding-Mode Control System Design in Fuel Cell Hybrid Electric Vehicles
N Qayyum, L Khan, M Wahab, S Mumtaz, N Ali, BS Khan
World Electric Vehicle Journal 16 (7), 351
2
Cites
2025
Year
Soft Computing Technique based Nonlinear Sliding Mode Control for Stand-Alone Photovoltaic System
AU Rehman, L Khan, N Ali, Z Alam, ZA Khan, MA Khan
2020 International Conference on Emerging Trends in Smart Technologies
2
Cites
2020
Year