quadrotor | Machine learning for High speed quadrotor flips | Machine Learning library

I'm trying to build a controller for a unity gameobject to move it similary to a normel quadrotor drone. So the drone should be able to ascend and descend by mobing the left stick up and down, turn around by moving the left stick sidewards and move horizontally by using the right stick.

I tried implementing it with the unity inputSystem, but unfortunately, it doesn't really work the way it is supposed to. Movements are mostly not smooth and the rotation causes the horizonzal movement to move in wrong directions.

Here is my code:

Consider an existing Drake System (for example MultibodyPlant). Is there a way to wrap that System inside a Diagram in such a way as to convert some of the states of the internal System to be inputs instead, i.e. set directly from input ports of the outer Diagram?

The motivation would be essentially to make a change in modeling decisions. For example, a quadrotor is sometimes considered to have its angular rates and collective thrust as inputs, instead of the collective thrust and body moments (or similarly, individual rotor commands).

In a more complex system, perhaps I might assume I have instantaneous control over certain velocities (e.g. internal, fully-actuated joints with fast dynamics), but still want to model the entire multibody system's dynamics accounting for the current choice of velocity in terms of Coriolis terms etc.

What I'm getting at is actually very similar to the modeling choice for the elevator in the Flat-Plate Glider Model - but I'd like to avoid manually implementing a LeafSystem because my system has nontrivial multibody dynamics.

My sense is that this may not be possible since I don't know of any way for a Diagram to interfere with the internal dynamics of a System, so "deleting" a state and promoting it to an input seems impossible. But I thought there might be some clever method to do this.

Thanks in advance!