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Found 11 papers, 6 papers with code
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores
The MagNet Challenge 2023 calls upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferrite cores.
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs).
Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control
Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications.
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
This review paper systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives.
Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning
Reinforcement learning (RL) is currently a popular research topic in control engineering and has the potential to make its way to industrial and commercial applications.
Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly.
Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control
Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas.
Systems and Control Systems and Control
Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part I)
Two of the most important aspects of electric vehicles are their efficiency or achievable range.
Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part II)
This document describes how to use the published data set \cite{Dataset} and how to extract models using introductory examples.
Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning
In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles.
Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control
An intelligent controller example based on the deep deterministic policy gradient algorithm which controls a series DC motor is presented and compared to a cascaded PI-controller as a baseline for future research.
