root/pmsm/mpf_u_delta_real.cpp @ 254

/*!
  \file
  \brief TR 2525 file for testing Toy Problem of mpf for Covariance Estimation
  \author Vaclav Smidl.

  \ingroup PMSM

  -----------------------------------
  BDM++ - C++ library for Bayesian Decision Making under Uncertainty

  Using IT++ for numerical operations
  -----------------------------------
*/


#include <itpp/itbase.h>
#include <estim/libKF.h>
#include <estim/libPF.h>
#include <estim/ekf_templ.h>
#include <stat/libFN.h>

#include "pmsm.h"
#include "simulator.h"
#include "sim_profiles.h"

using namespace bdm;

class IMpmsm_delta :  public IMpmsm {
protected:
        vec ud;
public:
        IMpmsm_delta() :IMpmsm(),ud ( 2 ) {ud=zeros ( 2 );};
        //! Set mechanical and electrical variables

        void condition ( const vec &val ) {ud = val;}
        vec eval ( const vec &x0, const vec &u0 ) {
                // last state
                double iam = x0 ( 0 );
                double ibm = x0 ( 1 );
                double omm = x0 ( 2 );
                double thm = x0 ( 3 );
                double uam = u0 ( 0 );
                double ubm = u0 ( 1 );

                vec xk=zeros ( 4 );
                //ia
                xk ( 0 ) = ( 1.0- Rs/Ls*dt ) * iam + Ypm/Ls*dt*omm * sin ( thm ) + ( uam+ud ( 0 ) ) *dt/Ls;
                //ib
                xk ( 1 ) = ( 1.0- Rs/Ls*dt ) * ibm - Ypm/Ls*dt*omm * cos ( thm ) + ( ubm+ud ( 1 ) ) *dt/Ls;
                //om
                xk ( 2 ) = omm + kp*p*p * Ypm/J*dt* ( ibm * cos ( thm )-iam * sin ( thm ) ) - p/J*dt*Mz;
                //th
                xk ( 3 ) = thm + omm*dt; // <0..2pi>
                if ( xk ( 3 ) >pi ) xk ( 3 )-=2*pi;
                if ( xk ( 3 ) <-pi ) xk ( 3 ) +=2*pi;
                return xk;
        }

};


int main() {
        // Kalman filter
        int Ndat = 8000; //1e6/125
        double h = 1e-6;
        int Nsimstep = 125;
        int Npart = 20;

        mat Rnoise = randn ( 2,Ndat );

        // internal model
        IMpmsm fxu0;
        IMpmsm_delta fxu;
        //                  Rs    Ls        dt       Fmag(Ypm)    kp   p    J     Bf(Mz)
        fxu.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 );
        fxu0.set_parameters ( 0.28, 0.003465, Nsimstep*h, 0.1989, 1.5 ,4.0, 0.04, 0.0 );
        // observation model
        OMpmsm hxu;

        vec mu0= "0.0 0.0 0.0 0.0";
        vec Qdiag ( "0.7 0.7 0.01 0.0001" ); //zdenek: 0.01 0.01 0.0001 0.0001
        vec Rdiag ( "0.1 0.1" ); //var(diff(xth)) = "0.034 0.034"
        chmat Q ( Qdiag );
        chmat R ( Rdiag );
        EKFCh KFE ( rx,ry,ru );
        KFE.set_parameters ( &fxu0,&hxu,Q,R );
        KFE.set_est ( mu0, chmat ( ones ( 4 ) ) );

        RV rUd ( "{ud }", "2" );
        EKFCh_cond KFEp ( rx,ry,ru,rUd );
        KFEp.set_parameters ( &fxu,&hxu,Q,R );
        KFEp.set_est ( mu0, chmat ( ones ( 4 ) ) );

        mlnorm<ldmat> evolUd ( rUd,rUd );
        MPF<EKFCh_cond> M ( rx,rUd,evolUd,evolUd,Npart,KFEp );
        // initialize
        vec Ud0="0 0";
        evolUd.set_parameters ( eye ( 2 ), vec_2 ( 0.0,0.0 ), ldmat ( 10.0*eye ( 2 ) ) );
        evolUd.condition ( Ud0 );
        epdf& pfinit=evolUd._epdf();
        M.set_est ( pfinit );
        evolUd.set_parameters ( eye ( 2 ), vec_2 ( 0.0,0.0 ), ldmat ( 0.005*eye ( 2 ) ) );

        mat Xt=zeros ( Ndat ,4 ); //true state from simulator
        mat Dt=zeros ( Ndat,2+2 ); //observation
        mat XtE=zeros ( Ndat, 4 );
        mat Qtr=zeros ( Ndat, 4 );
        mat XtM=zeros ( Ndat,2+4 ); //W + x
        mat XtMTh=zeros ( Ndat,1 );

        // SET SIMULATOR
        pmsmsim_set_parameters ( 0.28,0.003465,0.1989,0.0,4,1.5,0.04, 200., 3e-6, h );
        vec dt ( 2 );
        vec ut ( 2 );
        vec xt ( 4 );
        vec xtm=zeros ( 4 );
        double Ww=0.0;
        vec vecW="1 2 4 8 4 2 0 -4 -9 -16 -4 0 0 0";
        vecW*=10.0;

        it_file RD("tec0006ALL.it");
        mat RIsx, RIsy, RUsx, RUsy;
        RD >> Name("Isx") >> RIsx;
        RD >> Name("Isy") >> RIsy;
        RD >> Name("Usx") >> RUsx;
        RD >> Name("Usy") >> RUsy;
        
        for ( int tK=1;tK<Ndat;tK++ ) {
                //Number of steps of a simulator for one step of Kalman
                for ( int ii=0; ii<Nsimstep;ii++ ) {
                        //simulator
                        pmsmsim_noreg_step ( RUsx(125*tK+ii) , RUsy(125*tK+ii));
                };
                ut ( 0 ) = RUsx(125*tK);
                ut ( 1 ) = RUsy(125*tK);
                dt ( 0 ) = RIsx(125*tK);
                dt ( 1 ) = RIsy(125*tK);
                xt = vec ( x,4 );

                //estimator
                KFE.bayes ( concat ( dt,ut ) );
                M.bayes ( concat ( dt,ut ) );

                Xt.set_row ( tK, xt ); //vec from C-array
                Dt.set_row ( tK, concat ( dt,ut));
                XtE.set_row ( tK,KFE._e()->mean() );
                XtM.set_row ( tK,M._e()->mean() );
                // correction for theta

                {
                        double sumSin=0.0;
                        double sumCos=0.0;
                        vec mea ( 4 );
                        vec* _w;

                        for ( int p=0; p<Npart;p++ ) {
                                mea = M._BM ( p )->_e()->mean();
                                _w = M.__w();
                                sumSin += ( *_w ) ( p ) *sin ( mea ( 3 ) );
                                sumCos += ( *_w ) ( p ) *cos ( mea ( 3 ) );
                        }
                        double Th = atan2 ( sumSin, sumCos );

                        XtMTh.set_row ( tK,vec_1 ( Th ) );
                }

        }

        it_file fou ( "mpf_u_delta_real.it" );

        fou << Name ( "xth" ) << Xt;
        fou << Name ( "Dt" ) << Dt;
        fou << Name ( "xthE" ) << XtE;
        fou << Name ( "xthM" ) << XtM;
        fou << Name ( "xthMTh" ) << XtMTh;
        //Exit program:

        return 0;
}