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matlab_utilities/fuzlinear.cpp

MEX file that calculartes the linealization of a Fuzzy Model in a point.

MATLAB help:

% FUZLINEAR Calculartes the linealization of a Fuzzy Model in a point.
%
% A = fuzlinear(Fuzzy_Model, X, U)
%
% [A,B] = fuzlinear(Fuzzy_Model, X, U)
%
% [A,B,F] = fuzlinear(Fuzzy_Model, X, U)
%
% Solve the matrices the linear representation of the 'Fuzzy_Model' model
% according to Taylor series in the point X with signal control U.
% F is the value of the function at the point (X,U).
%
% Y = F + Ax + Bu
%
% Arguments:
%
% Fuzzy_Model -> Input Fuzzy model.
%
% X -> State vector.
%
% U -> Control vector.
%
% Fuzzy_Model -> This fuzzy model could be a '.txt' file, a '.fis' file,
% or a 'FIS' variable from MATLAB Workspace.
%
% See also activation, antec2mat, aproxjac, aproxlinear, conseq2mat, fis2txt,
% fuz2mat, fuzcomb, fuzeval, fuzjac, fuzprint, mat2antec, mat2conseq,
% mat2fuz, fuzsubsystem, txt2fis

Source code:

/* Copyright (C) 2004-2011
ANTONIO JAVIER BARRAGAN, antonio.barragan@diesia.uhu.es
http://uhu.es/antonio.barragan
Collaborators:
JOSE MANUEL ANDUJAR, andujar@diesia.uhu.es
MARIANO J. AZNAR, marianojose.aznar@alu.uhu.es
DPTO. DE ING. ELECTRONICA, DE SISTEMAS INFORMATICOS Y AUTOMATICA
ETSI, UNIVERSITY OF HUELVA (SPAIN)
For more information, please contact with authors.
This software is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This software is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "aux_matlab.hpp"
using namespace FLT;
using namespace TNT;
void mexFunction (int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
size_t i,q,j,n,m=0,model,countA,countB;
double *X,*U=NULL,*M_A,*M_B,*M_F;
System S;
if(nrhs<=2 || nrhs>3)
ERRORMSG(E_NumberArgIn)
if (nlhs>3)
ERRORMSG(E_NumberArgOut)
for (i=0;i<nrhs;i++)
{
if (mxIsEmpty(prhs[i]) || mxIsNaN(*mxGetPr(prhs[i])))
ERRORMSG(E_ArgNoValid)
}
X = mxGetPr(prhs[1]);
if (!X)
ERRORMSG(E_NumberArgIn)
if (mxGetN(prhs[1])!=1)
ERRORMSG(E_Column)
if (readModel(prhs[0],S))
ERRORMSG(E_Model)
n = mxGetM(prhs[1]);
if (n!=S.outputs())
ERRORMSG(E_PointCoherent)
if (nrhs==3)
{
U = mxGetPr(prhs[2]);
if (!U)
ERRORMSG(E_NumberArgIn)
if (mxGetN(prhs[2])!=1)
ERRORMSG(E_Column)
m = mxGetM(prhs[2]);
if (m!=(S.inputs()-n))
ERRORMSG(E_PointCoherent)
}
else if(m)
{
U = new double[m];
for (i=0;i<m;i++)
U[i] = 0;
}
else
U = NULL;
Array2D<double> B(n,m);
Array1D<double> F(n);
Array2D<double> A = jacobian(S,X,U,B,F);
if(!A.dim1())
{
if (nrhs==2 && m!=0)
delete []U;
mxDestroyArray(plhs[0]);
if (nlhs>1)
mxDestroyArray(plhs[1]);
if (nlhs==3)
mxDestroyArray(plhs[2]);
ERRORMSG(U_Overflow)
}
plhs[0] = mxCreateDoubleMatrix(n,n,mxREAL);
M_A = mxGetPr(plhs[0]);
if (!plhs[0] || !M_A)
{
if (nrhs==2 && m!=0)
delete []U;
mxDestroyArray(plhs[0]);
ERRORMSG(E_NumberArgOut)
}
if (nlhs>1 && nrhs==3)
{
plhs[1] = mxCreateDoubleMatrix(n,m,mxREAL);
M_B = mxGetPr(plhs[1]);
if (!plhs[1] || !M_B)
{
if (nrhs==2 && m!=0)
delete []U;
mxDestroyArray(plhs[0]);
mxDestroyArray(plhs[1]);
ERRORMSG(E_NumberArgOut)
}
}
if (nlhs==3)
{
plhs[2] = mxCreateDoubleMatrix(n,1,mxREAL);
M_F = mxGetPr(plhs[2]);
if (!plhs[2] || !M_F)
{
if (nrhs==2 && m!=0)
delete []U;
mxDestroyArray(plhs[0]);
mxDestroyArray(plhs[1]);
mxDestroyArray(plhs[2]);
ERRORMSG(E_NumberArgOut)
}
}
for (q=0,countA=0;q<n;q++)
{
for (i=0;i<n;i++,countA++)
*(M_A+countA) = A[i][q];
}
if (nlhs>1)
{
for (j=0,countB=0;j<m;j++)
for (i=0;i<n;i++,countB++)
*(M_B+countB) = B[i][j];
if (nlhs==3)
for (i=0;i<n;i++,countB++)
M_F[i] = F[i];
}
if (nrhs==2 && m!=0)
delete []U;
}