Related papers: Modelling of a DC-DC Buck Converter Using Long-Sho…
The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in the observed dynamical data, which is…
As a typical switching power supply, the DC-DC converter has been widely applied in DC microgrid. Due to the variation of renewable energy generation, research and design of DC-DC converter control algorithm with outstanding dynamic…
Reinforcement learning (RL) control approach with application into power electronics systems has become an emerging topic whilst the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature.…
Neural network-based optimization and control methods, often referred to as black-box approaches, are increasingly gaining attention in energy and manufacturing systems, particularly in situations where first-principles models are either…
A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter…
Artificial neural networks are a promising technique for virtual analog modeling, having shown particular success in emulating distortion circuits. Despite their potential, enhancements are needed to enable effect parameters to influence…
This study presents the modeling, control design, and performance analysis of a DC-DC buck converter using state-space averaging techniques. Buck converters are essential in modern power electronics for regulating DC voltages in renewable…
Predicting future physical behavior from limited theoretical simulation data is an emerging research paradigm driven by the integration of artificial intelligence and quantum physics. In this work, charge transport (CT) behavior was…
The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems. We show that suitable sufficient…
This paper presents comparative performance between Analog and digital controller on DC/DC buck-boost converter four switch. The design of power electronic converter circuit with the use of closed loop scheme needs modeling and then…
In order to address the challenge of traditional sliding mode controllers struggling to balance between suppressing system jitter and accelerating convergence speed, a deep neural network (DNN)-based sliding mode control strategy is…
Rapid developments in power distribution systems and renewable energy have widened the applications of dc--dc buck--boost converters in dc voltage regulation. Applications include vehicular power systems, renewable energy sources that…
To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised…
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on…
With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples. For data-free black box attack scenario, existing methods are inspired by…
This paper addresses the problem of regulating the output voltage of a DC-DC buck-boost converter feeding a constant power load,which is a problem of current practical interest. Designing a stabilising controller is theoretically…
This paper analyzes the stability-related properties of Long Short-Term Memory (LSTM) networks and investigates their use as the model of the plant in the design of Model Predictive Controllers (MPC). First, sufficient conditions…
Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of…
Digital control has become increasingly widespread in modern power electronic converters. When acquiring feedback signals such as the inductor current, synchronizing the analog-to-digital converter (ADC) with the digital pulse-width…
Numerical modeling of different structural materials that have highly nonlinear behaviors has always been a challenging problem in engineering disciplines. Experimental data is commonly used to characterize this behavior. This study aims to…