sident

Scilab Function

sident - discrete-time state-space realization and Kalman gain

Calling Sequence

[(A,C)(,B(,D))(,K,Q,Ry,S)(,rcnd)] = sident(meth,job,s,n,l,R(,tol,t,Ai,

Ci,printw))

Parameters

meth : integer option to determine the method to use:

= 1 : MOESP method with past inputs and outputs;
= 2 : N4SID method;
= 3 : combined method: A and C via MOESP, B and D via N4SID.

job : integer option to determine the calculation to be performed:

= 1 : compute all system matrices, A, B, C, D;
= 2 : compute the matrices A and C only;
= 3 : compute the matrix B only;
= 4 : compute the matrices B and D only.

s : the number of block rows in the processed input and output block Hankel matrices. s > 0.
n : integer, the order of the system
l : integer, the number of the system outputs
R : the 2*(m+l)*s-by-2*(m+l)*s part of R contains the processed upper triangular factor R from the QR factorization of the concatenated block-Hankel matrices, and further details needed for computing system matrices.
tol : (optional) tolerance used for estimating the rank of matrices. If tol > 0, then the given value of tol is used as a lower bound for the reciprocal condition number; an m-by-n matrix whose estimated condition number is less than 1/tol is considered to be of full rank. Default: m*n*epsilon_machine where epsilon_machine is the relative machine precision.
t : (optional) the total number of samples used for calculating the covariance matrices. Either t = 0, or t >= 2*(m+l)*s. This parameter is not needed if the covariance matrices and/or the Kalman predictor gain matrix are not desired. If t = 0, then K, Q, Ry, and S are not computed. Default: t = 0.
Ai : real matrix
Ci : real matrix
printw : (optional) switch for printing the warning messages.

= 1: print warning messages;
= 0: do not print warning messages.

Default: printw = 0.

A : real matrix
C : real matrix
B : real matrix
D : real matrix
K : real matrix, kalman gain
Q : (optional) the n-by-n positive semidefinite state covariance matrix used as state weighting matrix when computing the Kalman gain.
RY : (optional) the l-by-l positive (semi)definite output covariance matrix used as output weighting matrix when computing the Kalman gain.
S : (optional) the n-by-l state-output cross-covariance matrix used as cross-weighting matrix when computing the Kalman gain.
rcnd : (optional) vector of length lr, containing estimates of the reciprocal condition numbers of the matrices involved in rank decisions, least squares, or Riccati equation solutions, where lr = 4, if Kalman gain matrix K is not required, and lr = 12, if Kalman gain matrix K is required.

Description

SIDENT function for computing a discrete-time state-space realization (A,B,C,D) and Kalman gain K using SLICOT routine IB01BD.

                 [A,C,B,D] = sident(meth,1,s,n,l,R)
   [A,C,B,D,K,Q,Ry,S,rcnd] = sident(meth,1,s,n,l,R,tol,t)
                     [A,C] = sident(meth,2,s,n,l,R)
                         B = sident(meth,3,s,n,l,R,tol,0,Ai,Ci)
         [B,K,Q,Ry,S,rcnd] = sident(meth,3,s,n,l,R,tol,t,Ai,Ci)
                     [B,D] = sident(meth,4,s,n,l,R,tol,0,Ai,Ci)
       [B,D,K,Q,Ry,S,rcnd] = sident(meth,4,s,n,l,R,tol,t,Ai,Ci)

SIDENT computes a state-space realization (A,B,C,D) and the Kalman predictor gain K of a discrete-time system, given the system order and the relevant part of the R factor of the concatenated block-Hankel matrices, using subspace identification techniques (MOESP, N4SID, or their combination).

The model structure is :

         x(k+1) = Ax(k) + Bu(k) + Ke(k),   k >= 1,
         y(k)   = Cx(k) + Du(k) + e(k),

where x(k) is the n-dimensional state vector (at time k),

u(k) is the m-dimensional input vector,

y(k) is the l-dimensional output vector,

e(k) is the l-dimensional disturbance vector,

and A, B, C, D, and K are real matrices of appropriate dimensions.

COMMENTS

1. The n-by-n system state matrix A, and the p-by-n system output matrix C are computed for job <= 2.

2. The n-by-m system input matrix B is computed for job <> 2.

3. The l-by-m system matrix D is computed for job = 1 or 4.

4. The n-by-l Kalman predictor gain matrix K and the covariance matrices Q, Ry, and S are computed for t > 0.

Examples

//generate data from a given linear system
A = [ 0.5, 0.1,-0.1, 0.2;
      0.1, 0,  -0.1,-0.1;      
     -0.4,-0.6,-0.7,-0.1;  
      0.8, 0,  -0.6,-0.6];      
B = [0.8;0.1;1;-1];
C = [1 2 -1 0];
SYS=syslin(0.1,A,B,C);
nsmp=100;
U=prbs_a(nsmp,nsmp/5);
Y=(flts(U,SYS)+0.3*rand(1,nsmp,'normal'));

S = 15;
N = 3;
METH=1;
[R,N1] = findR(S,Y',U',METH);
[A,C,B,D,K] = sident(METH,1,S,N,1,R);
SYS1=syslin(1,A,B,C,D);
SYS1.X0 = inistate(SYS1,Y',U');

Y1=flts(U,SYS1);
xbasc();plot2d((1:nsmp)',[Y',Y1'])



METH = 2;
[R,N1,SVAL] = findR(S,Y',U',METH);
tol = 0;
t = size(U',1)-2*S+1;

[A,C,B,D,K] = sident(METH,1,S,N,1,R,tol,t)
SYS1=syslin(1,A,B,C,D)
SYS1.X0 = inistate(SYS1,Y',U');

Y1=flts(U,SYS1);
xbasc();plot2d((1:nsmp)',[Y',Y1'])

Author