1. Course 2: Construction of robot model overview; Projects specification (application);

2. Modeling

3. Modeling

4. Modeling

5. Modeling
A particular phenomenon is represented through the model. The model is a particular knowledge assembly, obtained by approximation process, that aspire to become operational;
Scientific knowledge must be validated continuously by the experiment;
It has as aim the a priori knowledge, more precisely we wish to know how will ensuing the phenomenon before the experimentation (voire pour prevoire).

6. Modeling The art of modeling: Oaccam (1285–1349) razor:
“Entities should not be multiplied unnecessarily.”
“When you have two competing theories which
make exactly the same predictions, the one that is
simpler is the better”
KISS: kipped smart and simple

7. Modeling S
Output
Input
Perturbation

8. Modeling: NM
Causes
Effects
Black Box

9. NM: Case study qm-1 q2 j=m-1 q1 j=2 j=1 q0 j=0 (x,y,z)i1…im  (x,y,z)D

10. NM: Case study

11. NM: Case study

12. Modeling Desires and results…an all time story u y
y= effects y= DESIRE
u= cause
What are the causes which can
produce my desire?

13. Modeling: The feedback
Controler
Plant
Feedback e
Senzor + - r
The control system
r  y
Perturb y
e  0

14. Modeling: J. Watt governor
Desire:
constant angular speed for the shaft

15. Modeling: The robot r y
link i-1
link i C - + u

16. Modeling: the robot Ac Tr
MSt Tk
V(t)
T(t)
(t)
q(t)
x(t)

17. Modeling: the robot C E S MR Pr qd P q e v  qo qp + - q1 q2 1 2 3

18. Conclusions