We have seen the inverse dynamics in Syria, so let's now apply it in practice focus or in MATLAB.
So first of all, how we will do it, we will first import the model of the desire to the robot manipulator.
In our case, I will just import at the model of you are five robotic manipulator, then justified the
PD gains, which are required.
And this is the time span to execute the whole process.
OK, so let's we just call the smoothing file and simulate the smerling file, and then we open the
scopes in order to see them.
So when you execute this one, it will be executed, and at the end you will see automatically the results
from the scopes.
But I will not do that because it will take a bit of time.
I have done this before and I will show you the results.
But let's first analyze the simulation file.
As you can see again, we have here our robot manipulator.
Again, this accept talks and gives out as GIS give out back the cute and cuddly, which are configuration,
velocity and acceleration, but even not actually if you will not need acceleration.
So we put the Terminator here in order to avoid the burning in the lab.
I'm sorry, I'm a bit of sick, so I am sick.
So that's why my throat is a bit bad.
I'm sorry for that.
Anyway, this is our desired trajectory.
OK, here is our desired orientation, velocity and acceleration.
As you can see, we have used we have used three or three of them, OK, we use configuration, velocity
and acceleration.
We have used velocity to calculator in order to calculate the input for the controller and excuse me,
configuration for peak controller velocity for the controller.
OK.
And we used also velocity acceleration feed forward.
OK.
Please remember this.
We use also acceleration feed forward.
OK.
And as you can see what we have done, we have to calculate the inverse dynamics here and we give it
to our robot, the main operator, OK, as an input torque.
So what we have done, we have tried to compensate for the terms in the modeling of the robotic manipulator
again, not just for the gravity term.
OK, let's see.
And again, here you have the try to visualize what's our error configuration error and what's our velocity
error?
OK?
Let's check if the voltage desired configuration or not.
OK.
This is so if you press run button, you will see the results, but I will not do that.
That's just directly check, as you can see, as you can see, no, our error is much, much smaller.
OK, let's check this is ten or minus three, OK?
As you can see these, our error is very small.
OK, from here, take this OK, and let's zoom it and let's check.
OK.
As you can see, the error at the end is 10 to the minus four, which is very, very small.
As you can see the almost zero 10 to the minus five.
OK, it's almost zero.
This controller is better than shear lipide controller.
OK?
This is better than PD controller.
And let's so we did.
We reached the desired configuration.
Let's see what's happening with the velocity.
As you can see, this is our velocity.
And as you can see at the end, we have reached the velocity error of zero.
And we this is the velocity of zero because at the end, we have to reach the velocity of zero if our
robot stops.
OK, so it start from the zero velocity or any initial velocity, it doesn't matter.
However, at the end it reaches the desired configuration, so it stops and the velocity becomes zero,
or it can reach even the desired velocity.
So the velocity error will be zero.
It depends just that it changed because of the task.
OK.
Or as you can see, we have reached the error of zero, both in terms of velocity tracking and configuration
tracking.
And again, you can just do some manipulation with b d gains and you can check what's happening with
the result and figures.