Adaptative high-gain extended Kalman filter and applications

tel-00559107, version 1 - 24 Jan 2011
int DCmachine bloc ending ( s c i c o s b l o c k ∗ block , int f l a g )
{return 0;}
C.3 AEKF Computational Functions C Code
C.3 AEKF Computational Functions C Code
In the following code, we used the notation:
− the array Ab[.] is composed of the elements of the matrix A(u)+b ∗ (x, u),
− the variables of the form Pij are the elements of the matrix P ,
− the tuning matrices of the EKF are set to r = 1 and Q = diag({q1,q2,q3}).
Those two matrices are organized as follows:
⎛
Ab[0] Ab[3] 0
⎞
⎝ Ab[1] Ab[4] Ab[7] ⎠ and
Ab[2] Ab[5] Ab[8]
⎛
⎝
P 11 P 21 P 31
P 21 P 22 P 23
P 31 P 23 P 33
function name AEKF number of zero crossing
surfaces
0
implicit n initial discrete state []
input port size 3 real parameter vector []
output port size 4 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.2: Arguments of the Main Function.
⊛ =[I(0); ωr(0); Tl(0); P (0); θ(0)], initial state is a dim 10 vector.
#include
#include
#include
/∗ i n p u t = [V;Y; I ( t ) ] ∗/
void AEKF( s c i c o s b l o c k ∗ xblock , int f l a g )
{
int n=3;
int N=n ∗( n+1) / 2 ;
if ( f l a g ==4)
{/∗ INITIALISATION ∗/
/∗ change o f v a r i a b l e s from o r i g i n a l c o o r d i n a t e s −> normal ..
form c o o r d i n a t e s ∗/
157
⎞
⎠