From e37f1817b8f1f6715c4c5014374f492c0c0c6898 Mon Sep 17 00:00:00 2001 From: CharlesCNorton <135471798+CharlesCNorton@users.noreply.github.com> Date: Wed, 20 Nov 2024 06:06:04 -0500 Subject: [PATCH] Update README.md Fix typos in documentation: - Corrected "*Faram* Gymnasium Environments" to "*Farama* Gymnasium Environments". - Fixed "Externally *exited* synchronous motor (EESM)" to "Externally *excited* synchronous motor (EESM)". - Updated "Journal of Open *Sorce* Software (JOSS)" to "Journal of Open *Source* Software (JOSS)". --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b92a5874..e10d18a6 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ ## Overview The gym-electric-motor (GEM) package is a Python toolbox for the simulation and control of various electric motors. -It is built upon [Faram Gymnasium Environments](https://gymnasium.farama.org/), and, therefore, can be used for both, classical control simulation and [reinforcement learning](https://github.com/upb-lea/reinforcement_learning_course_materials) experiments. It allows you to construct a typical drive train with the usual building blocks, i.e., supply voltages, converters, electric motors and load models, and obtain not only a closed-loop simulation of this physical structure, but also a rich interface for plugging in any decision making algorithm, from linear feedback control to [Deep Deterministic Policy Gradient](https://spinningup.openai.com/en/latest/algorithms/ddpg.html) agents. +It is built upon [Farama Gymnasium Environments](https://gymnasium.farama.org/), and, therefore, can be used for both, classical control simulation and [reinforcement learning](https://github.com/upb-lea/reinforcement_learning_course_materials) experiments. It allows you to construct a typical drive train with the usual building blocks, i.e., supply voltages, converters, electric motors and load models, and obtain not only a closed-loop simulation of this physical structure, but also a rich interface for plugging in any decision making algorithm, from linear feedback control to [Deep Deterministic Policy Gradient](https://spinningup.openai.com/en/latest/algorithms/ddpg.html) agents. In addition, an automated framework for classical control structures based on PI controllers is provided. ## Getting Started @@ -82,14 +82,14 @@ A GEM environment consists of following building blocks: Among various DC-motor models, the following AC motors - together with their power electronic counterparts - are available: - Permanent magnet synchronous motor (PMSM) - Synchronous reluctance motor (SynRM) -- Externally exited synchronous motor (EESM) +- Externally excited synchronous motor (EESM) - Squirrel cage induction motor (SCIM) - Doubly-fed induction motor (DFIM) The converters can be driven by means of a duty cycle (continuous control set) or switching commands (finite control set). ### Citation -A white paper for the general toolbox in the context of drive simulation and control prototyping can be found in the [Journal of Open Sorce Software (JOSS)](https://joss.theoj.org/papers/10.21105/joss.02498). Please use the following BibTeX entry for citing it: +A white paper for the general toolbox in the context of drive simulation and control prototyping can be found in the [Journal of Open Source Software (JOSS)](https://joss.theoj.org/papers/10.21105/joss.02498). Please use the following BibTeX entry for citing it: ``` @article{Balakrishna2021, doi = {10.21105/joss.02498},