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‫Welcome back.

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‫Congratulations, you have now absorbed quite a lot of knowledge and skills about drones, you now know

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‫how to mathematically model ID and how the entire plan works.

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‫You know what to do when the controller gives you the control inputs.

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‫Which were you one, you two, you three and you four.

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‫And now you know what to do when you have to convert them into your twelve states, six in the body

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‫frame.

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‫They were U, V, W, P, Q and R.

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‫So these three are the translation of the last piece of the drone in the body frame.

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‫So in the body frame, X direction, Y direction and Z direction.

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‫And then these three are the angular velocity of the drone in the body frame about the body frame x

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‫axis, y axis and z axis.

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‫This is in meters per second and this is in radians per second.

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‫And then you also had the states in the initial frame.

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‫You had capital X, capital Y, capital Z, and then also Phi Theta and BPCI.

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‫These were the drones position values in the inertia frame and you would measure them in meters and

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‫then these would be the drones orientation angles and then you would measure them in radians.

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‫So in the body frame, our states were in meters per second, in radians per second, but in the initial

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‫frame our states were in meters and in radians.

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‫And remember, it wasn't enough to just say the angles in order to determine the way these orientation.

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‫You also had to specify which angle you would rotate about first, which one would be the second and

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‫which one would be the third.

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‫And then you also had to specify whether you would rotate about the fixed axis or moving body frame

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‫axis.

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‫And we followed a convention that was R sub, A, Z, Y and X.

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‫These are lowercase letters, which means that we followed the oilor approach.

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‫So we rotate about the moving frame axis first, we rotate about the Z axis, the moving frame Z axis,

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‫then we rotate about the moving frame Y axis, and then we rotate about the moving frame x axis.

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‫And now it is time to leave the plan box alone and start unpacking the controller box.

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‫This was our plan here.

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‫And now since we're done with our plant now, we're going to leave it alone and we're going to see what

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‫inside here, the controller.

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‫In the autonomous vehicle, Laro Control, we only had one control input, the steering wheel angled

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‫Delta, this angle here was the control input that entered into the plant and then we had four states.

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‫We had our why dot lower case letter.

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‫Then we had our BPCI, then side dot and then the capital y letter.

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‫So this was the lateral velocity then.

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‫This was the angle then this is the angular velocity.

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‫And then finally this was the inertial Y.

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‫However, now we will have four control inputs.

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‫You want you to Q3 and Q4.

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‫So before we only had Delta and now we have these four control inputs.

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‫And instead of four states that we had in the previous course in this series, we will have 12 states.

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‫So obviously now the entire system and the controller is a lot bigger.

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‫In fact, you will see that you will have two controllers there working together while controlling the

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‫drone.

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‫So two controllers and one of them you already know, it's called MPAC or more predictive control.

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‫And the other one is called feedback liberalisation.

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‫And we're going to treat them in the next sections.

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‫In this section, however, we are going to look at the general structure of the controller.

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‫It will be a global overview of it.

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‫We will see how these two controllers work together.

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‫And so let's do it in the next video.

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‫Thank you very much.