From ffc2c1207cdd5b1a503e41719264566be4e5b907 Mon Sep 17 00:00:00 2001 From: Agostino De Marco Date: Mon, 14 Aug 2023 12:04:21 +0200 Subject: [PATCH] Update README.md Added a mention to the scientific article appeared on Nonlinear Dynamics in 2023 --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index c4052618b7..cf8395090b 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,11 @@ JSBSim is used in a range of projects among which: * Machine Learning Aircraft control: [gym-jsbsim](https://github.com/galleon/gym-jsbsim) * [DARPA Virtual Air Combat Competition](https://www.darpa.mil/news-events/2019-10-21) where one of the AI went undefeated in five rounds of mock air combat against an Air Force fighter (see the [video on YouTube](https://www.youtube.com/watch?v=IOJhgC1ksNU)). -JSBSim is also used in academic and industry research ([more than 700 citations referenced by Google Scholar](https://scholar.google.com/scholar?&q=jsbsim) as of May 2022). +## Academic and Industry Research + +JSBSim is also used in academic and industry research ([more than 700 citations referenced by Google Scholar](https://scholar.google.com/scholar?&q=jsbsim) as of May 2023). + +In 2023 JSBSim has been featured in the article ["A deep reinforcement learning control approach for high-performance aircraft"](https://link.springer.com/article/10.1007/s11071-023-08725-y) on _Nonlinear Dynamics_, an International Journal of Nonlinear Dynamics and Chaos in Engineering Systems by Springer. The open access article is available as a PDF here [https://link.springer.com/content/pdf/10.1007/s11071-023-08725-y.pdf](https://link.springer.com/content/pdf/10.1007/s11071-023-08725-y.pdf). The work demonstrates an application of Deep Reinforcement Learning (DRL) to flight control and guidance, leveraging the JSBSim interface to MATLAB/Simulink. # User Guide