‫Welcome back.
‫Congratulations, you have now absorbed quite a lot of knowledge and skills about drones, you now know
‫how to mathematically model ID and how the entire plan works.
‫You know what to do when the controller gives you the control inputs.
‫Which were you one, you two, you three and you four.
‫And now you know what to do when you have to convert them into your twelve states, six in the body
‫frame.
‫They were U, V, W, P, Q and R.
‫So these three are the translation of the last piece of the drone in the body frame.
‫So in the body frame, X direction, Y direction and Z direction.
‫And then these three are the angular velocity of the drone in the body frame about the body frame x
‫axis, y axis and z axis.
‫This is in meters per second and this is in radians per second.
‫And then you also had the states in the initial frame.
‫You had capital X, capital Y, capital Z, and then also Phi Theta and BPCI.
‫These were the drones position values in the inertia frame and you would measure them in meters and
‫then these would be the drones orientation angles and then you would measure them in radians.
‫So in the body frame, our states were in meters per second, in radians per second, but in the initial
‫frame our states were in meters and in radians.
‫And remember, it wasn't enough to just say the angles in order to determine the way these orientation.
‫You also had to specify which angle you would rotate about first, which one would be the second and
‫which one would be the third.
‫And then you also had to specify whether you would rotate about the fixed axis or moving body frame
‫axis.
‫And we followed a convention that was R sub, A, Z, Y and X.
‫These are lowercase letters, which means that we followed the oilor approach.
‫So we rotate about the moving frame axis first, we rotate about the Z axis, the moving frame Z axis,
‫then we rotate about the moving frame Y axis, and then we rotate about the moving frame x axis.
‫And now it is time to leave the plan box alone and start unpacking the controller box.
‫This was our plan here.
‫And now since we're done with our plant now, we're going to leave it alone and we're going to see what
‫inside here, the controller.
‫In the autonomous vehicle, Laro Control, we only had one control input, the steering wheel angled
‫Delta, this angle here was the control input that entered into the plant and then we had four states.
‫We had our why dot lower case letter.
‫Then we had our BPCI, then side dot and then the capital y letter.
‫So this was the lateral velocity then.
‫This was the angle then this is the angular velocity.
‫And then finally this was the inertial Y.
‫However, now we will have four control inputs.
‫You want you to Q3 and Q4.
‫So before we only had Delta and now we have these four control inputs.
‫And instead of four states that we had in the previous course in this series, we will have 12 states.
‫So obviously now the entire system and the controller is a lot bigger.
‫In fact, you will see that you will have two controllers there working together while controlling the
‫drone.
‫So two controllers and one of them you already know, it's called MPAC or more predictive control.
‫And the other one is called feedback liberalisation.
‫And we're going to treat them in the next sections.
‫In this section, however, we are going to look at the general structure of the controller.
‫It will be a global overview of it.
‫We will see how these two controllers work together.
‫And so let's do it in the next video.
‫Thank you very much.