Author here. I've spent the last six months replicating the paper "Champion-level drone racing using deep reinforcement learning" and now I'm writing down the blog posts I wish I had along the way. Any feedback is welcome, especially as I'm a bit unsure if I struck the right balance between being concise and not requiring too many prerequisites. Also if you're working on RL and robotics (especially aerial), let's con…
I assume you are going to start introducing all the 2nd and 3rd order effects? One big one is ground effect, and another is vortex ring state/settling with power and the related translational lift, and the props themselves have p-factor and the dirty air effect for the rear props.
In general, I think I'd try to go for a black-box/grey-box model based on real data rather than e.g. CFD-based, as I don't think you can run CFD at sufficient accuracy for real-time control anyway. For that, I would look at https://rpg.ifi.uzh.ch/docs/TRO26_Bauersfeld.pdf or https://rpg.ifi.uzh.ch/docs/RSS21_Bauersfeld.pdf