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Optimal Adaptive Control of Power Converters Using Reinforcement Learning

https://doi.org/10.2139/ssrn.4758756
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inferredcites →https://doi.org/10.1002/9781118453988openalex-extracted
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
inferredcites →https://doi.org/10.1016/j.apenergy.2021.117541openalex-extracted
A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning
inferredcites →https://doi.org/10.1016/j.apenergy.2023.120707openalex-extracted
Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning
inferredcites →https://doi.org/10.1016/j.automatica.2015.10.039openalex-extracted
Model-based reinforcement learning for approximate optimal regulation
inferredcites →https://doi.org/10.1016/j.conengprac.2023.105499openalex-extracted
Voltage Control of a Quasi Z-Source Converter Under Constant Power Load Condition Using Reinforcement Learning
inferredcites →https://doi.org/10.1016/j.engappai.2023.105833openalex-extracted
Voltage control of DC–DC converters through direct control of power switches using reinforcement learning
inferredcites →https://doi.org/10.1016/j.rser.2023.113627openalex-extracted
Review of online learning for control and diagnostics of power converters and drives: Algorithms, implementations and applications
inferredcites →https://doi.org/10.1016/j.solener.2019.07.021openalex-extracted
Constant switching frequency finite control set model predictive control applied to the boost converter of a photovoltaic system
inferredcites →https://doi.org/10.1109/cdc.2010.5717148openalex-extracted
Concurrent learning for convergence in adaptive control without persistency of excitation
inferredcites →https://doi.org/10.1109/mie.2015.2478920openalex-extracted
Model Predictive Control: MPC's Role in the Evolution of Power Electronics
inferredcites →https://doi.org/10.1109/tcsii.2019.2955711openalex-extracted
Continuous Nonsingular Terminal Sliding Mode Control of DC–DC Boost Converters Subject to Time-Varying Disturbances
inferredcites →https://doi.org/10.1109/tie.2007.909058openalex-extracted
General Design Issues of Sliding-Mode Controllers in DC–DC Converters
inferredcites →https://doi.org/10.1109/tie.2008.2008349openalex-extracted
Model Predictive Control—A Simple and Powerful Method to Control Power Converters
inferredcites →https://doi.org/10.1109/tie.2020.3005071openalex-extracted
A Novel Nonlinear Deep Reinforcement Learning Controller for DC–DC Power Buck Converters
inferredcites →https://doi.org/10.1109/tnnls.2017.2773458openalex-extracted
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
inferredcites →https://doi.org/10.1109/tpel.2006.882929openalex-extracted
A Digital Current-Mode Control Technique for DC–DC Converters
inferredcites →https://doi.org/10.1109/tpel.2007.915765openalex-extracted
Boundary Control of Boost Converters Using State-Energy Plane
inferredcites →https://doi.org/10.1109/tpel.2012.2199334openalex-extracted
Control of Power Converters in AC Microgrids
inferredcites →https://doi.org/10.1109/tpel.2014.2347396openalex-extracted
An Extended Lyapunov-Function-Based Control Strategy for Single-Phase UPS Inverters
inferredcites →https://doi.org/10.1109/tpel.2015.2431193openalex-extracted
Second-Order Sliding-Mode Controlled Synchronous Buck DC–DC Converter
inferredcites →https://doi.org/10.1109/tpel.2020.2977765openalex-extracted
DC/DC Power Converter Control-Based Deep Machine Learning Techniques: Real-Time Implementation
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