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‫Welcome back.

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‫So we have just covered an enormous section in how to derive the kinematics and dynamics, fundamental

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‫equations we learned about rotation and transfer matrices in order to connect and be able to transfer

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‫information between the inertial and body frames.

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‫And then I showed you how the Newton oilor formulation in the body frame for a six degree of freedom

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‫system is achieved.

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‫It was a big section, however, it was also a necessary one.

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‫Without that foundation, you would not fully understand the Oves plan model.

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‫And so now I am going to derive a specific plan model for our USV.

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‫Using the results from the previous section, you will see that you have already done the hard part.

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‫Applying stuff from the previous section to our USV will be very easy.

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‫So let's get onto it.

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‫In the end of the last section, we had a Newton oilor formulation that looked like this, and then

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‫we explored an alternative form for it, which looks like this, this equation is the exact same equation

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‫like this first one.

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‫The only thing is that it is written in a different form.

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‫So this matrix M in the B frame, it was this matrix here, this entire one, then this vector here

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‫would be this one.

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‫Then this vector here would be this entire thing here.

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‫And the biggest difference was the second term that appeared because the body frame rotated with respect

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‫to the inertia frame.

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‫So we took this term here and we converted it into this term here and now we are going to take this

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‫alternative form and work with that.

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‫Now, in principle, our goal is the same that we had when we worked with the vehicle in case of a vehicle,

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‫we went from the equations of motion.

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‫That looked like this.

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‫To the states based representation that looked like this in red.

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‫If you remember then the A, B, C, D, E and F.

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‫Constance were composed of many variables related to the vehicle, and since all those variables were

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‫themselves constant, including X Dot and I in the B frame, then D, A, B, C, D, E, F variables

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‫were also fixed values.

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‫And that's why we call them constants.

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‫Not that they don't have to be constant, they are constant in our case, thanks to assuming that our.

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‫X double dot or our longitudinal acceleration was zero meters per second squared.

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‫In other words, our longitudinal velocity was constant and that's why our A, B, C, D, e, f variables

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‫were constants.

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‫We learned that state's basic equations are first order differential equations because you only have

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‫there and input Delta states, why dad and BPCI that.

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‫And the first time derivative of the states, why double dot inside, double dot y double that can be

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‫rewritten in this form here and BPCI double dot in this form here.

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‫And when you take that first order differential equation and you express the time derivative of the

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‫states as a function of states themselves and the inputs like this.

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‫Why double date as a function of why that side dot and Delta equals something and then double that as

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‫a function of why dot?

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‫Cidade and Delta equals something else.

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‫Then you have a state based equation.

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‫In general variables, it would look like this if you assign X one to Y dot, X to topside that and

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‫then you to Delta.

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‫Then this is how it would look like like this.

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‫So even though you have a double time derivative here, then don't confuse that thing, because remember

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‫that other states, they already have one time derivative.

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‫So when you have a double time derivative here, then the difference is only one derivation with respect

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‫to time, and that's why it's a first order differential equation.

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‫If instead of why, that you would simply have why and then here you had why double that, then it would

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‫not be a first order differential equation, then it would be a second order differential equation,

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‫especially if you had another term, like, for example, why that here?

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‫So you would have y y dot and then y double dot.

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‫That wouldn't be the first order differential equation and therefore it wouldn't be a state based equation.

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‫A state's basic equation is a first order differential equation where you have the states, the inputs

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‫and then the time derivatives of the states and you express the time derivatives of the states as a

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‫function of the states and the inputs.

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‫And now we want to have the same thing in our USV case.

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‫This is our Newton oilor formulation, the alternative form of ID Lambda B that has three net force

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‫elements in it affects F, Y and Z and it has three net moment elements in it and X and Y and then Mzee.

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‫And remember, these are all net values, meaning that each element here is a summation of forces in

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‫their respective dimensions or moments in their respective dimensions.

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‫Then this is our state vector.

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‫In this state, Victor has six elements.

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‫Three for translational velocity in the body frame.

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‫You've N.W. meters per second and three, four angular velocities in the body frame, so P, Q and R.

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‫Radians per second, this vector looks like this, and then the time derivative of our states is this

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‫vector here.

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‫That's how it looks like.

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‫The first three elements here are translational acceleration in the body frame and then the last three

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‫are the angular acceleration in the body frame.

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‫And so what we want to do is essentially the same thing.

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‫We want to express the state time derivatives in terms of their states and inputs.

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‫So in other words, we want to put our Newton oilor equations of motion in the state's space form.

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‫So the data vector B equals everything else.

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‫And so you have an exercise now you have this equation here, this alternative Newton oilor form.

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‫And so your task now is to take this entire form and you have to put it in the same space form.

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‫Now, don't bother with specifics.

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‫This is a one minute exercise and you can do it in two or three operations.

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‫I just want you to do it before I show it to you in the next video.

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‫So give it a try.

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‫Thank you very much.

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‫And I see you in the next video.

