Adaptative high-gain extended Kalman filter and applications

tel-00559107, version 1 - 24 Jan 2011
C.2 Computational Function for the Simulation of the DC Machine
if exists ( ’ outport ’ ) then model . out=ones ( outport , 1 ) , ..
model . i n = [ ] ,
else model . out =1, model . i n = [ ] , end
model . e v t i n=1
model . rpar =[]
model . i p a r =[ch ;
range ;
a r e f ;
length (name) ;
a s c i i (name) ’ ]
model . d s t a t e = [ 1 ] ;
model . blocktype= ’ d ’
model . dep ut =[%t %f ]
exprs =[sci2exp ( ch ) ,name , sci2exp ( range ),sci2exp ( a r e f ) ]
gr i =[ ’ x s t r i n g b ( o r i g ( 1 ) , o r i g ( 2 ) , [ ’ ’COMEDI A/D ’ ’ ; name+ ’ ’ ..
CH− ’ ’+s t r i n g ( ch ) ] , sz ( 1 ) , sz ( 2 ) , ’ ’ f i l l ’ ’ ) ; ’ ]
x=standard define ( [ 3 2 ] , model , exprs , g r i)
end
endfunction
C.2 Computational Function for the Simulation of the DC
Machine
This first code are given only to provide a complete picture with regard to the implementation.
This simulation presents no difficulties. Let us remark that an alternative solution to the
definition of the parameter in the code itself is to pass them through the real parameter vector
entry of the interfacing function, see [41].
function name DCmachine number of zero crossing
surfaces
0
implicit n initial discrete state []
input port size 2 real parameter vector []
output port size 2 integer parameter vector
[]
input event port size [] initial firing vector []
output event port size [] direct feed through y
initial continuous state ⊛ time dependence y
Table C.1: Arguments of the DC Simulation.
⊛ =[I(0), ωr(0)].
#include
#include
#include
void DCmachine ( s c i c o s b l o c k ∗ block , int f l a g )
{ /∗ t h i s i s t h e l i s t o f t h e f i e l d s we have to use
155