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| function [th,ice,M]=ivgpmf(z,nn,Ts,param,n0,ngpmf,methody,methodu,ic,Tsy,Tsu);
%IVGPMF Computes the continuous-time MISO model parameters from sampled I/O data
% by the Auxiliary Model Instrumental-Variable Generalized Poisson Moment Functionals Approach.
%
% numi(s) -nki.Ts b0i.s^m + ... + bmi
% Gi(s) = ------- = e . ------------------- with a0=1
% den(s) a0.s^n + ... + an
%
% Y(s) = G1(s).U1(s) + ... + Gnu(s).Unu(s)
%
% [th,ice]=ivgpmf(z,nn,Ts,param,n0,ngpmf,'methody','methodu','ic',Tsy,Tsu) returns
% the estimated numerators and denominator under the theta form and the estimated
% initial conditions.
%
% num1 = [b01 ... bm1], ..., numi= [b0i ... bmi]
% den = [1 a1 ... an]
%
% z : the output-input data z=[y u], with y and u as column vectors.
% nn : nn = [n m nk], the orders of the above model.
% n : order of denominator
% m : order of numerator contains as many columns as number of inputs
% nk : delay of the model contains as many columns as number of inputs
% (integer number of sampling period Ts)
% Ts : sampling period (s) at which i/o data are acquired
% param : vector of the Poisson filter parameters [lambda beta]
%
% optional inputs:
% n0 : starting estimation index
% ngpmf : order of the GPMF (minimal-order GPMF then ngpmf=n)
% methody : method used for the simulation of the Poisson filter chain for the output signal
% 'lsim' : simulation using the lsim.m function
% 'tustin' : simulation using the bilinear approximation
% methodu : method used for the simulation of the Poisson filter chain for the input signal
% 'zoh' : simulation assuming a zero order hold on the I/O data
% 'lsim' : simulation using the lsim.m function
% 'tustin' : simulation using the bilinear approximation
% 'ic' : if mentioned, the initial conditions are estimated
% Tsy : Sampling period for the digital simulation of output signal Poisson filter Chain
% Tsy must be smaller than Ts then Spline interpolation is used to minimize numerical
% errors introduced in the simulation of the PFC
% Tsu : Sampling period for the digital simulation of input signal Poisson filter Chain
% Tsu must be smaller than Ts then Spline interpolation is used to minimize numerical
% errors introduced in the simulation of the PFC
% See for further explanations :
% Garnier H.,
% "Identification de modèles paramétriques continus par moments de Poisson",
% Thèse de Doctorat de l'Université Henri Poincaré, Nancy 1, 1995.
% authors : Hugues Garnier - Michel Mensler
% date : 29 Oct. 1996
% revision : 8 Dec. 1998
% name : ivgpmf.m
%
% CRAN - Research Center in Automatic Control of Nancy
% e-mail : <a href="mailto:hugues.garnier@cran.u-nancy.fr">hugues.garnier@cran.u-nancy.fr</a>
%*** Preliminary calculations ***
if nargin<4,error('Number of input arguments incorrect!'),return,end
[Ncap,nz]=size(z); [nr,nc]=size(nn);
ny=1;
nu=(nz-1);
if nz>Ncap,error('Data should be organized in column vectors!'),return,end
if nc<3, nn(3)=0; end
m=nn(2:nu+1);n=nn(1);nk=nn(nu+2:2*nu+1);
nkmax=max(nk);
for counter=1:length(nk),
u(:,counter)=z(nkmax-nk(counter)+1:Ncap-nk(counter),counter+1);
end
y=z(nkmax+1:Ncap,1);
N=size(u,1); % Number of data points
lambda=param(1);
beta=param(2);
if (nargin<11)
Tsu=[];
if (nargin<10)
Tsy=[];
if (nargin<9)
ic=' ';
ice='no estimation of the ic';
if nargin<8
methodu='lsim';
if nargin<7
methody='lsim';
if nargin<6
ngpmf=n;
if nargin<5
n0=1;
end
end
end
end
end
end
end
if ngpmf<n,error('The GPMF order should be greater or equal to the model order n!'),return,end
if Tsy>Ts,
error('The sampling period Tsy should be smaller than the original sampling period Ts of the data!'),
return,end
if Tsu>Ts,
error('The sampling period Tsu should be smaller than the original sampling period Ts of the data!'),
return,end
T=0:Ts:(N-1)*Ts; % time vector
Tma=T; % time vector for the simulation of the auxiliary model
npu=n+1;
% 1. Generation of the I/O data moment functionals
% 1.1 State-Space Representation of the filter chain
I=eye(ngpmf+1);
D=diag([beta*ones(1,ngpmf)],-1)-lambda*I;
Q=[beta zeros(1,ngpmf)]';
% 1.2 Simulation of the I/O data filter chain for the output signal
if (isempty(Tsy)) % Simulation of the PFC at the original sampling time
Tsy=Ts;
Ty=T;
Int_Ratio=1; % Ratio for the output signal interpolation
else % New sampling time for the numerical simulation of the I/O PFC
Int_Ratio=Ts/Tsy;
Ti = (0:Tsy:T(N))';
y = interp1(T,y,Ti,'spline'); % New interpolated output vector
Ty=Ti;
end
Du=zeros(ngpmf+1,1);
if methody(1)=='l' % lsim
Yg=lsim(D,Q,I,Du,y,Ty);
elseif methody(1)=='t' % tustin
[Df,Qf,Cf,Dff]=c2dm(D,Q,I,Du,Tsy,'tustin');
Yg=dlsim(Df,Qf,Cf,Dff,y);
end
Yg=Yg(1:Int_Ratio:length(Yg),ngpmf+1-n:ngpmf+1);
% 2. Generation of the Poisson pulse functions
if ic=='ic' % tests if the initial conditions are wanted
for i=1:ngpmf+1
for j=1:N
P(i,j)=((beta^i)*(((j-1)*Ts)^(i-1))*(exp(-lambda*(j-1)*Ts)))/prod([1:(i-1)]);
end
end
P=P(ngpmf+1-n:ngpmf+1,:);
end
% 3. Definition of the matrices LAM LAMu LAMy and LAMp which are involved in the
% computation of the measures vector
% 3.1 Definition of LAM
for i=1:npu
for j=1:npu
if i>j LAM(i,j)=0;
else LAM(i,j)=((-1)^(j-i))*(prod([1:npu-i])/(prod([1:npu-j])*...
prod([1:j-i])))*(lambda^(j-i))*((beta^(npu-j)));
end
end
end
% 3.3 Definition of LAMy = LAM without its first line
LAMy=LAM(2:npu,:);
% 3.4 Definition of LAMp = LAMy
LAMp=LAMy;
% 4. Generation of the measures vector M (M'.theta = Y)
My = -LAMy*Yg(n0:N,:)';
Mu=[];
Ym = (LAM(1,:)*Yg(n0:N,:)')';
%Simulation of the I/O data filter chain for the input signal
if (isempty(Tsu))
Tsu=Ts; % Simulation of the PFC at the original sampling time
Tu=T;
Int_Ratio=1; % Ratio for the output signal interpolation
uint=u;
else % sampling time for the numerical simulation of the I/O PFC
Int_Ratio=Ts/Tsu;
Ti = (0:Tsu:T(N))';
uint=interp1(T,u,Ti,'spline'); % New interpolated input vector
Tu=Ti;
end
for kk=1:nu,
if methodu(1)=='z' % zoh
[Df,Qf]=c2d(D,Q,Tsu);
Ug=dlsim(Df,Qf,I,Du,uint(:,kk));
elseif methodu(1)=='l' % lsim
Ug=lsim(D,Q,I,Du,uint(:,kk),Tu);
elseif methodu(1)=='t' % tustin
[Df,Qf,Cf,Dff]=c2dm(D,Q,I,Du,Tsu,'tustin');
Ug=dlsim(Df,Qf,Cf,Dff,uint);
end
Ug=Ug(1:Int_Ratio:length(Ug),ngpmf+1-n:ngpmf+1);
LAMu=LAM(npu-m(kk):npu,1:npu);
Mu = [Mu ; LAMu*Ug(n0:N,:)'];
end
% 4.1 with the initial conditions terms
if ic=='ic'
Mic = -LAMp*P(:,n0:N);
M = [My' Mu' Mic'];
else
% 4.2 without the initial conditions term
M = [My' Mu'];
end
[N_LS,n_par]=size(M); % N_LS : number of data used in the LS algorithm
% n_par : number of parameters to be estimated
% 5. Estimation by off-line LS
thmc=pinv(M)*Ym;
% 6. Simulation of the estimated system (yma:aux. model output)
[N_LS,n_par]=size(M); % N_LS : number of data used in the LS algorithm
% n_par : number of parameters to be estimated
II=[1 -Ts nu 0 m+1 0 0 n*ones(1,nu) nk];
nb=n+sum(m+1);
th=zeros(nb+3,max([length(II) nb 7]));
th(1,1:length(II))=II;
ti=fix(clock); ti(1)=ti(1)/100;
th(2,2:6)=ti(1:5);
th(2,7)=1;
thmc_i=[];
for counter=1:nu,
thmc_i=[thmc_i thmc(1:n)'];
end
th(3,1:nu*n+sum(m+1))=[thmc(n+1:n+sum(m+1))' thmc_i];
[Amc,Bmc,Cmc,Dmc]=th2ss(th);
yma=lsim(Amc,Bmc,Cmc,Dmc,u,Tma);
% 7. Generation of the moment functionals of the aux. model output
if (isempty(Tsy)) % Simulation of the PFC at the original sampling time
Tsy=Ts;
Ty=T;
Int_Ratio=1; % Ratio for the output signal interpolation
else % New sampling time for the numerical simulation of the I/O PFC
Int_Ratio=Ts/Tsy;
Ti = (0:Tsy:T(N))';
yma = interp1(T,yma,Ti,'spline'); % New interpolated output vector
Ty=Ti;
end
if methody(1)=='z'
Ygma=dlsim(Df,Qf,I,Du,yma);
elseif methody(1)=='l'
Ygma=lsim(D,Q,I,Du,yma,Ty);
elseif methody(1)=='t'
Ygma=dlsim(Df,Qf,Cf,Dff,yma);
end
Ygma=Ygma(1:Int_Ratio:length(Ygma),ngpmf+1-n:ngpmf+1);
% 8. Generation of the aux. model measures vector
Myma=-LAMy*Ygma(n0:N,:)';
if ic=='ic'
% 8.1 with the initial conditions terms
Mma=[Myma' Mu' Mic'];
else
% 8.2 without the initial conditions terms
Mma=[Myma' Mu'];
end
% 9. Estimation by aux. model IV
thiv=(Mma'*M)\Mma'*Ym;
I=[1 -Ts nu n m+1 0 0 zeros(1,nu) nk];
nb=n+sum(m+1);
th=zeros(nb+3,max([length(I) nb 7]));
th(1,1:length(I))=I;
ti=fix(clock); ti(1)=ti(1)/100;
th(2,2:6)=ti(1:5);
th(2,7)=2;
th(3,1:n+sum(m+1))=thiv(1:n+sum(m+1))';
if ic=='ic'
ice=thiv(npu+sum(m+1):length(thiv))';
end |
identification des systemes continus
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